Journal of Renal Nutrition
Volume 20, Issue 5 , Pages 281-292.e7, September 2010

Relationship Between Body Mass Index and Mortality in Adults on Maintenance Hemodialysis: A Systematic Review

  • Marietjie Herselman, PhD

      Affiliations

    • Division of Human Nutrition, Stellenbosch University, Tygerberg, South Africa
    • Tygerberg Academic Hospital, Tygerberg, South Africa
    • Corresponding Author InformationAddress reprints requests to Marietjie Herselman, PhD, Division of Human Nutrition, Faculty of Health Sciences, Stellenbosch University, PO Box 19063, Tygerberg 7505, South Africa.
    • ,
  • Nazeema Esau, BSc Diet

      Affiliations

    • Division of Human Nutrition, Stellenbosch University, Tygerberg, South Africa
    • Tygerberg Academic Hospital, Tygerberg, South Africa
    • ,
  • Jean-Marie Kruger, MNutr

      Affiliations

    • Division of Human Nutrition, Stellenbosch University, Tygerberg, South Africa
    • Tygerberg Academic Hospital, Tygerberg, South Africa
    • ,
  • Demetre Labadarios, PhD

      Affiliations

    • Tygerberg Academic Hospital, Tygerberg, South Africa
    • Knowledge Systems, Human Sciences Research Council, Cape Town, South Africa
    • ,
  • Mohammed Rafique Moosa, MD

      Affiliations

    • Tygerberg Academic Hospital, Tygerberg, South Africa
    • Department of Medicine, Stellenbosch University, Tygerberg, South Africa

published online 28 June 2010.

Article Outline

Objective

The primary objective of this systematic review was to determine the relationship between body mass index (BMI) and all-cause and cardiovascular mortality.

Design

Systematic review of primarily observational studies.

Patients

Adult patients from all gender, race, or ethnic groups on maintenance hemodialysis.

Methods

Medline, Science Citation Index, Academic Search Premier, Cochrane Library, and Embase electronic databases covering the period 1966 to December 2008 were searched with the help of a qualified librarian. Reference lists of included papers and collections also were searched. Each study was reviewed by 2 independent reviewers who also performed the data extraction from full papers. Differences between reviewers were resolved by consensus or by a third reviewer in the case of disagreements. The quality of studies selected for inclusion in the systematic review was also assessed by 2 independent reviewers.

Main Outcomes

BMI and mortality.

Results

Eighteen studies (60%) reported a significant inverse relationship between all-cause mortality and BMI. This inverse relationship was more prevalent in older patients, larger retrospective studies, and studies that did not adjust for inflammation. On the other hand, 57% of the 7 studies reporting on cardiovascular mortality found no significant relationship with BMI.

Conclusions

This systematic review shows evidence of an inverse relationship between BMI and all-cause mortality in adult patients on maintenance HD, especially in older patients, but the relationship with cardiovascular mortality is less clear.

 

This article has an online CPE activity available at www.kidney.org/professionals/CRN/ceuMain.cfm

IN THE GENERAL population, obesity is a well-known risk factor for diabetes, hypertension, and cardiovascular disease (CVD),1 and is also considered to be an independent risk factor for the development of chronic kidney disease (CKD).2, 3, 4 A recent systematic review confirmed an elevated risk for CKD in both overweight and obese individuals.5 Furthermore, it was estimated that as many as 34% of CKD cases in the United States and 25% in industrialized countries could be related to overweight and obesity. The association between body mass index (BMI) and CKD appears to be mediated by obesity-related glomerulopathy and enhanced glomerular blood pressure transmission,3 diabetes, and hypertension,6 as well as hypertriglyceridemia and low high-density lipoprotein cholesterol levels.7

In contrast to the general population, an inverse relationship between BMI and mortality has been reported in dialysis patients, even at BMI levels in the overweight and obesity range,8, 9 but the relationship appears to be inconsistent, at least in statistical terms.10, 11 Although the recommended goal in the general population is to maintain body weight at a BMI of 18.5 to 24.9,12 the benefits and safety of weight loss in the overweight dialysis patient is not known. The relationship between obesity and survival in older adults is also not clear. Currently health professionals are unsure regarding the need to treat obesity in dialysis patients, especially since the 2003 Kidney Disease Outcomes Quality Initiative (K/DOQI) Clinical Practice Guidelines for Managing Dyslipidaemias in Chronic Kidney Disease supports a BMI range of 25 to 28 in adults with CKD.13, 14

The mechanism(s) underlying the inverse relationship between BMI and mortality are not clear and are subject to controversy. CVD is known to be the number one killer in dialysis and transplant patients;14 hence, before a higher BMI target can be recommended with confidence, the potentially adverse effects of overweight and obesity such as hypertension, dyslipidemia, and CVD must be carefully considered.

In view of the prevailing uncertainty about the target BMI that should be used for dialysis patients, the nature of the relationship between BMI and mortality should be investigated. The primary objective of this systematic review was to determine the relationship between BMI and mortality, and to distinguish between all-cause and cardiovascular mortality in adult patients with end-stage renal failure on maintenance hemodialysis (HD). The secondary objective was to determine the influence of age, dialysis adequacy, comorbidity, study direction and duration, study size, inflammation, and study quality on the relationship between BMI and mortality.

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Methods 

Studies were limited to intervention, cohort, and case-control studies investigating the relationship between BMI and mortality in adult patients of all gender, race, or ethnic groups on maintenance HD. Only studies adapting for confounding factors and published in the English language were included.

The process followed in the selection of studies for the final review is shown in Figure 1 and has also been described elsewhere.15 We searched the Medline, Science Citation Index, Academic Search Premier, Cochrane Library, and Embase electronic data bases covering the period from 1966 to December 2008 with the help of a qualified librarian, using a controlled vocabulary of Medical Subject Headings (MeSH) terms and free text, appropriately modified for the different data bases. Reference lists of included papers and own collections were also searched. Electronic search results were screened liberally from titles and abstracts by a single author to determine the relevancy of studies according to the inclusion criteria. This was followed by the selection of full papers for inclusion in the review. Each study was reviewed by 2 independent reviewers, and in cases where there was a discrepancy between the reviewers, the differences were resolved by consensus.

Two independent reviewers extracted the data from full papers using structured forms, which were adapted for renal failure from the guidelines by the Cochrane Non-randomized Studies Methods Group,16 the Cochrane Renal Group,17 the Centre for Reviews and Dissemination (CRD) Report Number 4,18 and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Checklist of essential items Version 3.19 These forms were piloted on a sample of 10 published studies on mortality in dialysis patients. Differences between reviewers were resolved by consensus or by a third reviewer in the case of disagreements. Reviewers paid attention to the possible inclusion of duplicate publications or overlap of databases,20 and authors were contacted for missing information, if required.

The quality of studies selected for inclusion in the systematic review was assessed by 2 independent reviewers and differences were resolved by discussion or by a third reviewer, if required. The Newcastle-Ottawa Scale21 was used to assess the quality of observational studies as recommended by the Cochrane Non-randomized Studies Methods Group.16 The quality of experimental studies was assessed according to the recommended guidelines.22, 23

Data analysis was performed with the Number Cruncher Statistical System (NCSS) Statistical and Power Analysis Software Package (NCSS, Kaysville, UT).24 To simultaneously explore the effects of several explanatory variables of mortality risk, only studies that adjusted for confounding factors were included in the systematic review. Extracted data were transferred to electronic spreadsheets on Excel and entries were checked independently for accuracy by a second author. Meta-analysis was attempted using the random effects model, but due to a high degree of heterogeneity (Cochran Q = 60.28, df = 13, P = .0000) the results are not reported here.

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Results 

Results of the Search 

The search resulted in a total of 2006 titles and abstracts (Fig. 1). During the initial screening of titles and abstracts, 1,560 titles and abstracts were excluded because they were not relevant or were considered to be duplicate publications on the same patient population. This resulted in 446 studies that underwent crude independent screening, resulting in a total of 176 studies that were considered for data extraction. During data extraction, a further 145 studies were excluded because they did not meet the selection criteria. This resulted in 31 studies which were subsequently included in the systematic review8, 9, 11, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52 and assessed for quality by 2 independent reviewers.

The reasons for excluding 145 studies from the systematic review are shown in Figure 2, keeping in mind that many studies had more than 1 reason for exclusion. The methodological quality of the observational studies included in the systematic review varied between 4 and 8 out of a total score of 9 points with the most frequent score being 6 (Fig. 3). The average score was 6 [SD 1]. There was 1 randomized controlled trial.52 As the scoring system for randomized controlled trials differs from that of observational studies, the score were excluded from these results. This study was adequate in terms of random allocation to groups, the intention-to-treat principle and completeness of follow-up but was unclear in terms of concealment of allocation and blinding procedures.

Causes of Death 

Causes of death were reported by 52% of the studies (Table 1, available only online at www.jrnjournal.org), but this was not done in a standardized manner which limits the interpretation of the data. For these studies, CVD was the most common reported cause of death at an average of 46% (range, 12% to 72%), followed by infections at 21% (range, 3% to 59%). Tumors were also a relatively frequent cause of death. CVD and tumors showed a trend to be a more common cause of death in studies where the average age was more than 60 years (55 [8]% and 10 [6]%, respectively) compared with younger patients (39 [16]% and 7 [3]%, respectively). In contrast, infections tended to be slightly more common in the younger (25 [18]%) compared with the older patients (18 [8]%).

Of the 31 studies reporting on BMI, 19 were prospective and 11 were retrospective cohort studies (Table 1, available online). One study was a randomized controlled study with retrospective pooling of BMI data for analysis of mortality risk.52 The total number of patients studied was 255,160, with the average age in the different studies varying between 43 and 83 years. Males varied from 43% to 73% in the different studies. Data on race and/or ethnicity was provided in 19 studies, whereas 12 studies did not describe the racial/ethnicity distribution (Table 1, available online). Three of these studies were done on exclusively white subjects.

Mortality Risk 

The majority of studies (60%) reported a significant inverse relationship between all-cause mortality and BMI for the combined group (Tables 1, available online, 2). This inverse relationship between all-cause mortality and BMI was more prevalent in the following studies: where the average age was more than 60 years 8, 9, 25, 26, 27, 28, 29, 31, 32, 47, 48, 50 compared with younger30, 43, 45, 46, 49, 52 patient populations (71% versus 46%), retrospective 8, 9, 43, 45, 46, 47, 48, 49, 50, 52 compared with prospective25, 26, 27, 28, 29, 30, 31, 32 studies (83% versus 44%), studies of larger 8, 9, 26, 27, 28, 30, 32, 43, 45, 47, 48, 49, 52 compared with smaller25, 29, 31, 46, 50 sample size (81% versus 36%), and those not adjusted 8, 8, 8, 9, 26, 27, 28, 30, 31, 32, 43, 45, 47, 48, 49, 50 compared with those adjusted25, 52 for inflammation as measured by C-reactive protein (CRP) and interleukin-6 (67% versus 29%). For the small subgroup of 7 studies that adjusted for CRP11, 33, 36, 38, 39, 52 and interleukin-6,25 5 studies (71%)11, 33, 36, 38, 39 did not find a significant relationship between BMI and all-cause mortality. Thirty-three percent of studies failed to find a significant relationship between BMI and all-cause mortality,26, 11, 33, 34, 35, 36, 37, 38, 39, 40 1 study reported a significantly higher mortality risk with a BMI of 23 and higher,44 and 1 study reported a significantly higher mortality risk with a BMI of 30 and higher.48 Studies varied widely in terms of the confounding factors used for adapting the results (Table 1, available online). Despite the majority of studies (64%) adjusting for the confounding effect of comorbidity, 8, 9, 11, 27, 28, 30, 31, 32, 37, 38, 43, 45, 47, 48, 49, 50, 52 78% of these studies still found a significant inverse relationship between BMI and mortality (Table 2).8, 9, 27, 28, 30, 31, 32, 43, 45, 47, 48, 49, 50, 52 Of the 10 studies that did not adjust for comorbidity,25, 26, 33, 34, 35, 36, 39, 40, 44 more than two thirds26, 33, 34, 35, 36, 39, 40 failed to find a significant relationship with mortality. The inverse relationship was similar in lower25, 26, 27, 28, 31, 32, 43, 46, 47, 50 compared with higher8, 9, 29, 30, 45, 48, 49 quality studies (59% and 58%, respectively), and in shorter 9, 25, 26, 27, 28, 30, 32, 43, 46, 47, 49, 50, 52 compared with longer-term8, 29, 31, 45, 48 studies (62% and 56%, respectively). Only 3 studies11, 32, 39 adjusted for Kt/V; therefore, it was not possible to determine a possible link with mortality.

Table 2. Summary of the Relationship Between Body Mass Index (BMI) and Mortality in Maintenance Hemodialysis
Study groupsSignificant Inverse Relationship (N (%) of Studies)Significant Positive Relationship (N (%) of Studies)No Significant Relationship (N (%) of studies)
All-cause mortality∗, †
All studies combined (N = 30)†18 (60)2 (7)10 (33)
Average age <60 years (N = 13)6 (46)1 (8)6 (46)
Average age ≥60 years (N = 17)12 (71)1 (6)4 (24)
Quality rating 3-6 (N = 17)‡10 (59)1 (6)6 (35)
Quality rating 7-9 (N = 12)‡7 (58)1 (8)4 (33)
Average/median study duration 0-3 years (N = 21)13 (62)–8 (38)
Average/median study duration >3-6 years (N = 9)5 (56)2 (22)2 (22)
Prospective studies (N = 18)8 (44)–10 (56)
Retrospective studies (N = 12)10 (83)2 (17)–
Adjusted for inflammation (CRP, IL-6) (N = 7)§2 (29)–5 (71)
Not adjusted for inflammation (CRP, IL-6) (N = 21)§14 (67)2 (10)5 (24)
Sample size ≤468 (N = 14)5 (36)1 (7)8 (57)
Sample size >468 (N = 16)13 (81)1 (6)2 (13)
Not adjusted for Kt/V (N = 25)§15 (60)2 (8)8 (32)
Adjusted for Kt/V (N = 3)§1 (33)–2 (67)
Not adjusted for co-morbidity (N = 10)§2 (20)1 (10)7 (70)
Adjusted for co-morbidity (N = 18)§14 (78)1 (6)3 (17)
Cardiovascular mortality∗
All studies combined (N = 7)3 (43)–4 (57)

CRP, C-reactive protein; IL, interleukin; Kt/V, dialysis adequacy.

∗Some studies reported on both all-cause and cardiovascular mortality29, 38, 48, 52 and some reported on cardiovascular mortality only 41, 42, 51

†One study reported an inverse relationship between BMI and mortality (continuous data) and increased mortality at a BMI >30 (categorical data)48 and another study reported increased mortality at a BMI <18.5 but not above 30.26

‡One study reported retrospective analysis of pooled data from a prospective randomized controlled trial52 and was excluded from subgroup analysis on study quality due to a different scoring process.

§Studies did not always clearly specify the details required for subgroup analysis for adjustment for confounding factors29, 46 and were excluded from subgroup analysis for these categories.

Closer inspection of individual studies reporting categorical data showed that the BMI threshold associated with mortality varied widely (Table 3). Four studies reported a significant increase in mortality risk with BMI values below 23.1,47 21.9,8 19.0,9 and 18.5,26 whereas 3 studies did not find a significant relationship between BMI values ≤20 and mortality.36, 37, 44 Decreased mortality was reported at BMI values of 25 to 30,9 above 27.8,47 and above 30.8, 9, 28 No significant relationship was found between mortality risk and BMI >30 in 2 studies,26, 37 and 1 study reported increased mortality at a BMI >30.48

Table 3. BMI Threshold and Relationship With All-cause Mortality
BMI Category∗RRHR95% CIP-valueNMean AgeReference
≥300.77––.0029,71461 (15)28
–1.200.83-1.74NS72266 (7)26
0.61––.019,71461 (15)28
0.89–0.58-1.37NS55364 (15)37
–0.890.81-0.99.0421,67561.7 (15.6)8
–0.850.808-0.896<.000166,59575.2 (−)9
–1.041.01-1.07<.02170,02864.8 (14.2)48
≥27.80.84––.000145,96764.2 (−)47
>25-29.90.61–0.42-0.90NS55364 (15)37
–0.950.78-1.16NS1,67561.7 (15.6)8
–0.8650.832-0.900<.000166,59575.2 (−)9
–0.940.87-1.01NS70,02864.8 (14.2)48
18.5-25Reference
≤23.11.19––.000145,96764.2 (−)47
≤21.9–1.411.15-1.71.0011,67561.7 (15.6)8
≤200.74–0.37-1.42NS18049 (14)36
0.98–0.64-1.50NS55364 (15)37
≤199.1–0.8-45.5NS11650.1 (1.1)44
–1.271.212-1.327<.000166,59575.2 (−)9
<18.5–2.001.118-3.39(Significant)72266 (7)26

∗Categories are approximate as very few studies used exactly the same cut-off points; only multivariate studies and those using BMI of 18.5-25 as reference are included in this table.

Only 7 studies included in the systematic review reported on cardiovascular mortality (Table 1, available online).29, 38, 41, 42, 48, 51, 52 Four of these studies (57%) found no significant relationship between cardiovascular mortality and BMI.38, 41, 42, 51 Because of the small number of studies reporting on cardiovascular mortality, no comparison of subgroups was attempted.

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Discussion 

Body Mass Index 

In this systematic review of primarily observational studies, the majority of studies showed a significant inverse relationship between BMI and all-cause mortality. This is in contrast to the “J” shaped curve between mortality and BMI reported for the general population, with the obese having the highest risk.53, 54, 55 At higher levels of the BMI, mortality is usually due to chronic diseases of lifestyle,56 whereas BMI levels below 18.5 is accompanied by an increased mortality risk due to other diseases such as digestive and pulmonary disease.53 Obesity has been associated with harmful changes in the cardiovascular system, even in the absence of clinical cardiac disease, and weight reduction has been associated with several cardiovascular benefits related to arterial pressure, pre- and after-load, stimulation of the cardiovascular system,57, 58, 59 and improvements in ventricular function.60

It is interesting to note that 57% of the 7 studies investigating CVD mortality in this review found no significant relationship between CVD mortality and BMI. Furthermore, and contrary to what is found in the general population, overweight and obesity were reported to be associated with improved survival in patients on maintenance HD.8, 9, 28, 47, 48, 61 In the study of Beddhu et al.,48 it was found, however, that the protective effect of a high BMI was limited to those patients with normal or high muscle mass, the latter being inferred from urinary creatinine excretion. A high BMI with high body fat, in contrast, was associated with increased and not decreased mortality. Before allowing dialysis patients to increase their body weight above the normal BMI range, one should also take cognizance of the potentially adverse effects of obesity in transplant patients. Obesity has been associated with a higher risk of complications during surgery,62 and transplantation itself is associated with further weight gain. In a review on the relationship between obesity and outcome of renal transplant patients, obesity prevalence of up to 19% has been reported and obesity was found to be a risk factor for higher overall mortality and chronic graft loss.63 Other studies reported that an increase in wound complications was the only significant adverse effect of obesity, and that excellent long-term patient and graft survival was possible in obese subjects.64

Several studies have documented a similar “obesity paradox” in patients with heart failure (often present in HD patients), with overweight and obese people showing a better prognosis compared with patients with normal weight.65, 66, 67, 68, 69, 70, 71, 72 In a retrospective study investigating the role of obesity in patients with heart failure, patients with a higher BMI showed a trend toward improved survival, compared with patients with normal BMI. Ejection fraction, etiology of heart failure and angiotensin-converting enzyme inhibitor use predicted survival in these patients but BMI did not.65 The mechanism for the “obesity paradox” is not known, and there is no clear-cut evidence of a causal relationship between obesity and mortality, but there are several possible explanations for this phenomenon.73

The systematic review data suggest that the inverse relationship between all-cause mortality and BMI was more prevalent in retrospective studies and studies with larger sample size. Large sample size may have been one of the reasons to explain the difference between retrospective and prospective studies in the systematic review, as 7 of the 11 retrospective studies compared with only 3 of the 19 prospective studies had sample sizes exceeding 1,000 subjects. Thus, in crude terms, the retrospective studies may have been more powerful than the prospective studies in detecting significant relationships because of their larger sample size. The recently published study by Chazot et al. is an example of a prospective study with a large sample size (5,592 subjects) where the reverse epidemiology of obesity and overweight was confirmed.74 Moreover, they reported lower survival in patients with a weight loss of more than 5.8% in the first year compared with patients with a stable weight.

Age-related Factors 

In this systematic review, the inverse relationship between all-cause mortality and BMI was more prevalent in studies where the average age of participants was 60 years and older. This was also the case in the recently published study by Chazot 2009 in a Southern European HD population with an average age of 64 years.74 In this “older” group, 80% of studies included in the systematic review reported a significant inverse relationship between BMI and mortality compared with only 50% in the younger groups. It is worth exploring in more detail the results of the 2 largest and most powerful studies included in this review.9, 48 Both studies received the same relatively high quality scores; gender and race were comparable and they used similar BMI cut-off points, but there was a 10-year difference in the average age of the participants. In the study of Xue et al.,9 66,595 patients with an average age of 75.2 years were studied. It was found that overweight and obesity were both associated with a decrease in mortality risk of 13% and 15%, respectively, whereas a low BMI of <19 was associated with a 27% increase in mortality risk. In the study of Beddhu et al.,48 70,028 patients with an average age of 64.8 years were studied. In this study, there was not a significant relationship between mortality and overweight, whereas obesity was associated with a significant increase in mortality. In contrast to these 2 studies, none of the studies done on patients with an average age <50 years showed a significant relationship between mortality and BMI.34, 36 This finding is in line with reports in other nonrenal populations, showing a protective effect of overweight and obesity in older people.75, 76, 77, 78

A recent study investigated the relationship between BMI and CVD risk at mean ages of 25, 47 (year 1974), and 73 years (year 2000) in a homogenous sample of 1,114 men without chronic disease.73 Between 1974 and 2000, men who were overweight in midlife and lost weight in late life had the greatest mortality risk. They also had the highest CVD risk in midlife while being overweight. In contrast, the risk of men who became overweight after midlife did not differ from that of men with a constantly normal weight. Similarly, in a California cohort study, decreased all-cause mortality was associated with changing from a normal weight in the twenties to gaining weight by late adulthood. Participants who were overweight or obese at a young age and those who lost weight between age 21 and study entry had an increased mortality irrespective of their BMI category at a young age.79

Weight loss is a known risk factor for mortality in the elderly, and mortality rates of 9% to 38% have been observed within one to two and a half years after weight loss.80, 81, 82 Unintentional weight loss may reflect severity of underlying diseases such as CVD, cancer, or infections, all common causes of death in the studies reported in this systematic review. In a longitudinal study on 983 community-dwelling older adults, low BMI and unintentional weight loss was found to be a greater mortality threat than obesity or intentional weight loss.83 The adverse effect of unintentional weight loss on mortality was also confirmed by others,84, 85, 86, 87, 88, 89, 90 whereas intentional weight loss in overweight subjects have failed to decrease CVD mortality.85, 86, 87, 88 This lack of benefit may indicate that the beneficial effects of weight loss on the cardiovascular system is limited to younger people,91 and that atherosclerotic vascular disease is not easily reversed once established. Although some studies showed that repeated intentional weight losses were not predictive of greater all-cause or CVD mortality,92 others have found that it increased risk of death in the elderly.93 None of the studies included in this systematic review have accounted for the effects of unintentional weight loss, which may be associated with a more advanced form of comorbidity. Thus the potential effect of intentional weight loss may have been masked by pooling of the results.

In a prospective cohort study of Chinese people aged 65 years or older,94 the relationship between obesity and mortality varied according to the underlying health status. BMI was inversely related to all-cause mortality, but the relationship varied with cause of death and with baseline health status. Respiratory mortality had a steep inverse gradient with BMI, whereas cardiovascular mortality had a shallower gradient. The investigators interpreted their results as suggesting that BMI at older ages is a marker of other factors, including fitness, immune status, and muscle mass, so that BMI in older people is an overall marker of the severity of disease.

Severity of Comorbidities Unrelated to Age 

Overweight and obese patients may represent patients with less severe comorbidities than their malnourished counterparts. However, obese patients have shown better survival than normal-weight patients with comparable severity of heart failure70 and following myocardial infarction after adjusting for severity of illness, age, and gender.67 The majority of studies have, however, adjusted for the confounding effect of comorbidity, and 78% of these studies still found a significant inverse relationship between BMI and mortality. In most studies, however, the adjustment procedures considered the existence, but not the severity, of comorbidities.

The Presence or Absence of Inflammation Unrelated to Age 

Subgroup analysis of studies included in the systematic review revealed that 5 of the 7 studies that adjusted for the presence of inflammation failed to find a significant relationship between BMI and mortality. This may be an indication that the inverse relationship between BMI and mortality may, at least partially, be explained by the presence or absence of the adverse effects of inflammation. Two types of malnutrition, starvation and cachexia, have been described in CKD.95, 96 Starvation in the form of poor energy intake is characterized by a lack of inflammation and normal albumin concentrations. In contrast, Cachexia is associated with systemic inflammation and elevated serum CRP levels, enhanced proteolysis, and increased oxidative stress. This form of malnutrition combined with inflammation (also called the malnutrition-inflammation complex syndrome) is highly predictable of increased mortality.97 None of the studies reported in this review distinguished between the 2 types of malnutrition and very few adjusted for the presence of inflammation. Because undernutrition predisposes to infection or renders patients more susceptible to the damage of inflammation, obesity may potentially attenuate the magnitude of protein-energy malnutrition or inflammation, resulting in a more favorable outcome of HD patients.97

Inflammation in HD patients may be related to processes associated with renal failure itself, it may be a consequence of dialysis, or it may be unrelated to renal failure and dialysis.98 Such causes may include oxidative injury, altered immune function attributed to uremia, accumulation of advanced glycation end-products, protein-energy malnutrition, the vascular access site, the dialysate, dialysate back leak, nonbiocompatible membranes,98, 99 and older age.100 The malnutrition-inflammation complex syndrome may well be an important contributory factor in the reverse epidemiology of obesity observed in dialysis patients because infection is a major cause of morbidity and mortality in dialysis patients (and this includes cardiovascular mortality), as confirmed by this systematic review.

Short-term Effect 

Between 46%101 and 65%102 of dialysis patients have been reported to die within 5 years of commencing dialysis treatment. In the presence of fluid overload, pulmonary edema, and heart failure (all frequent complications in maintenance HD patients), the 5-year survival is even worse at 20.2%, 21.3%, and 12.5% of patients, respectively.103 Therefore, it is possible that the short-term survival advantages of obese dialysis patients may outweigh the harmful effects of the traditional risk factors of CVD in the long-term because of to a shorter life expectancy. A recent study found that overweight and obese individuals were protected from short-term death but they had a long-term mortality risk similar to that of individuals of normal weight.104 In this systematic review, results were similar for studies of shorter (less than 3 years) compared with longer duration (more than 3 years).

Intensity of Treatment 

Overweight and obese patients are thought to be diagnosed with CVD at an earlier stage of their disease due to accentuation of symptoms of heart failure by obesity. They may therefore be treated earlier and more intensely than patients with a lower BMI.73 Many overweight and obese dialysis patients are also treated with angiotensin-converting enzyme inhibitors,97 which may have survival advantages.105

Survival or Selection Bias 

Finally, survival or selection bias may have influenced the results of epidemiological studies in which individuals who successfully survived traditional risk factors end up on dialysis.

Limitations 

This systematic review has several limitations and the results should be interpreted with caution. It is based primarily on observational studies because randomized controlled designs are not feasible with this type of study, and many of the studies were done retrospectively. Heterogeneity was high, and the search was limited to studies published in the English language. The effect of language bias has, however, diminished because of the shift toward publication of studies in English.106, 107 A common problem experienced in this review was the lack of adequate reporting of statistical data (e.g., CIs) and unclear methodology, which complicated the assessment of study quality. Studies also did not adjust for the same confounding factors and did not distinguish between intentional and unintentional weight loss. Finally, many studies may have scored low on quality not because of poor design but due to unclear or inadequate description of the methodology.

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Conclusions and Recommendations 

This systematic review provides evidence of an inverse relationship between BMI and all-cause mortality in adult patients on maintenance HD, especially in older patients. The relationship with cardiovascular mortality was however less clear. Although the apparent “obesity paradox” clearly represents an association between BMI and all-cause mortality, there is no evidence that this relationship is causal. In the studies included in this review, it is clear that CVD, infections, and to a lesser extent malignancy, were the main cause of mortality. In our opinion, BMI status most likely serves as a marker of disease severity, whereby unintentional weight loss caused by chronic disease or other comorbid conditions such as malignancy and infections might explain the inverse relationship between BMI and mortality. This hypothesis clearly needs to be confirmed by well-designed studies.

Meanwhile, maintaining a normal BMI and treatment of infections and undernutrition should continue to receive a high priority in the management of patients on HD. In older overweight/obese patients, there are indications that weight loss should not be a priority, unless associated risks require weight loss.

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Acknowledgments 

The authors thank Ms. Ingrid van der Westhuizen from the Library, Faculty of Health Sciences, Stellenbosch University for searching of data bases and Prof DG Nel, Stellenbosch University, for statistical analysis of the data. The authors' contributions were as follows: conception and design of the paper (M.H., N.E., D.L. and R.M.), the generation, collection, analysis and interpretation of the data (M.H., N.E., J.M.K., D.L. and R.M.), drafting of the manuscript (M.H.); and the approval of the final version of the manuscript (M.H., N.E., J.M.K., D.L. and R.M.).

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Table 1. 

Studies Included in the Systematic Review for BMI (N = 31)
Reference NumbersControl for Confounding FactorsMean Age in Years (SD)% MalesEthnicity (%)Average Duration of Follow-up (Years)Number Enrolled (Final Sample Size)Summary Statistic∗ (95% CI)P-ValueCauses of Mortality (%)Comments
Prospective Cohort Studies (N = 19)
25Age, BMI, albumin, IL-662 (−)43.3Black 58
White 32
Asian 10		2.590 (73)All-cause mortality CVD 59
Infection 27
GIT Surgery 14		Decreased all-cause mortality with higher baseline BMI
Risk ratio 0.911 (−).019
26Age, gender, smoking66 (7)58White2.6 (1.9)722All-cause mortality CVD 49Increase in 7-year all-cause mortality risk at baseline BMI <18.5 compared with BMI 22.5-25
No significant increase in 7-year all-cause mortality risk at baseline BMI >30
Reference BMI 22.5-25–
Baseline BMI <18.5
HR 2.00 (1.18-3.39)–
Baseline BMI >30
HR 1.2 (0.83-1.74)NS
27Age, race, gender, education, glomerulonephritis, diabetes, hypertension, polycystic kidney disease, HD sessions/week, time since onset of ESRD, BMI, albumin, creatinineNot clear (64.5-66.5)51Caucasian 50.5215,543 (15,067)All-cause mortality –Decrease in all-cause mortality risk with increase in baseline BMI
Prevalent: OR 0.971 (−)<.001
Incident: OR 0.965 (−)<.001
28Age, gender, race, country, smoking, serum albumin, 15 comorbidities61 (15)57Black 2339,714 (−)All-cause mortality –Decreased all-cause mortality with baseline BMI ≥30 compared twith BMI 23-24.9
BMI ≥30 compared to BMI 23-24.9
USA RR 0.77 (−).002
Europe RR 0.61 (−).01
29Not specified62.4 (13.7)54Whites 100>2280 (280)All-cause mortality Cardiovascular 58
Sepsis 18
Tumor 11
Other 13		Decreased all-cause and cardiovascular mortality with higher BMI
RR 0.87 (−).0004
Cardiovascular mortality
RR 0.85 (−).001
30BMI, age, diabetes, serum albumin, pre-albumin, creatinine, hematocrit, globulin, race56.1 (0.4)46Black 89
White 11		11,346 (1,089)All-cause mortality CVD 50-63
Infection 9-17
Tumor 3-6
Pulmonary 3
GIT 3-4
CRF 3-8
Aluminum disease 1-3
Intoxication 1-3
Trauma 1-2
Dementia 1
Sickle cell disease 1 Other 4-11		Decreased all-cause mortality with increasing BMI
RR 0.96 (0.93-0.99).0075
31Age, gender, need for dialysis catheter, late referral, BMI, functional dependence, peripheral vascular disease, heavy co-morbidity83.2 (2.9)55–12101 (101)All-cause mortality CVD 43
Tumor 20
Withdrawal 16
Sudden death 12
Infection 3
Other 7		Decrease in all-cause mortality risk with increase in BMI only in patients 0-12 months on dialysis
(0-12 months on dialysis)
HR 0.83 (0.73-0.95)<.01
(>12 months on dialysis)
HR 1.00 (0.91-1.10)NS
32Age, Davies comorbidity score, primary kidney disease, serum albumin, SGA, BMI, Kt/V, blood pressure, hemoglobin, calcium, phosphate, nPCR62.3 (13.9)57.4–Median 1.7740 (526)All-cause mortality –Decreased all-cause mortality with higher baseline BMI
RR 0.96 (0.93-0.99).0252
33Age, use of biocompatible membranes, albumin, creatinine, transferrin saturation, ferritin, corrected calcium, log iPTH, log hsCRP, nPCR, cardiothoracic ratio53.9 (0.6)48.9–2468 (468)All-cause mortality Infection 59
Cardiovascular 35
Other 7		No significant relationship with all-cause mortality
–NS
34BMI, sex, age, dialysis duration, blood pressure, serum levels of creatinine, lipids, PTH, calcium, phosphate, Ca X P, glucose, hematocrit, parathyroidectomyParathyroidectomy 43 (10)
No Parathyroidectomy 45 (12)		52Caucasian 68
Afr Braz 30
Oriental 2		Median 2.5 years118 (−)All-cause mortality –No significant relationship with all-cause mortality
–NS
35Age, sex60.5 (10.2)72.2-72.8–2.7150 (150)All-cause mortality CVD 43
Infection 19
Tumor 9
Bleeding 7
Liver disease 2
Other 9		No significant relationship between baseline BMI and all-cause mortality
HR survival 0.952 (0.882-1.027).199
36Age, gender, SGA, CRP, serum albumin, fibrinogen, BMI49 (14)55–1.7180 (159)All-cause mortality Cardiovascular 52
Infections 30
Other 18		No significant relationship with all-cause mortality
Baseline BMI ≤20 vs. >20
HR 0.74 (0.37-1.42).67
11Age, gender, HD duration, diabetes, hypertension, blood pressure, hemoglobin, Kt/V, BMI, PCR, serum albumin, total cholesterol, HDL, hsCRP, serum-MBL59.2 (12.9)56–3131 (−)All-cause mortality CVD 33
Infection 22
Sudden death 33		No significant relationship between baseline BMI and all-cause mortality
OR 0.748 (0.546-1.023).0694
37Gender, BMI, transplantation, renal replacement therapy duration, Charlson co-morbidity score64 (15)63Caucasian 983553 (541)All-cause mortality –No significant relationship between baseline BMI and all-cause mortality
BMI 20-25
Reference–
BMI <20
HR 0.98 (0.64-1.50).93
BMI 26-29
HR 0.61 (0.42-0.90).60
BMI >29
HR 0.89 (0.58-1.37).1
38Age, gender, smoking, dialysis vintage, CRP, hypertension, diabetesMedian 66.7 (22.3-93.5)47–5190 (187)All cause mortality CVD 60
Infection 16
Tumor 8
Cahexia 8
Other 9		No significant relationship between baseline BMI and all-cause or cardiovascular mortality
BMI <21.24
Reference–
BMI 21.24-25.27
RR 1.24 (0.71-2.14)NS
BMI >25.27
RR 1.11 (0.65–1.90)NS
Cardiovascular mortality
BMI <21.24
Reference–
BMI 21.24-25.27
RR 1.34 (0.66-2.76)NS
BMI >25.27
RR 1.10 (0.54-2.21)NS
39Age, sex, race, renal disease, SGA, BMI, body fat, dialysis malnutrition score, near infrared interactance (body fat and lean body mass), serum albumin, CRP, urea, Kt/V55.8 (15.3)53Black 48
Hispanic 24
Asian 23		183 (−)All-cause mortality –No significant relationship with all-cause mortality
HR 0.71 (0.28-1.82).48
40Age, severity index, albumin, dialyzer type, dialysis site55.1 (14.3)72.9Afr-Am 92.13.3240 (−)All-cause mortality –No significant relationship with all-cause mortality
RR 1.00 (0.96–1.05).80
41Age, sex, diabetes, smoking, BMI, systolic blood pressure, cholesterol, CRP, fibrinogen, HD duration, hemoglobin, albumin, Ca X P product59.5 (15.1)56Caucasian 1002.4175 (−)Cardiovascular mortality CVD 61
Infection 10
Cachexia 8
Tumor 6
Hyperkalemia 6
Bleeding 4
Respiratory failure 2
Diabetic coma 2
Treatment withdrawal 2		No significant relationship with cardiovascular mortality
HR 1.05 (0.95-1.15).38
42Age, sex, HD duration, diabetes, blood pressure, BMI, serum cholesterol, intima-media thickness of carotid artery59.7 (12.2)60–2.5438 (438)Cardiovascular mortality CVD 54
Infection 22
Tumor 7
Other 17		No significant relationship with cardiovascular mortality
HR 0.90 (0.79-1.02).103
Retrospective Cohort Studies (N = 11)
8Demographic variables, comorbidities, medication, SGA, BMI, serum albumin and cholesterol, independent walking.61.7 (15.6)53Afr-Am 33.751,675 (1,295)All-cause mortality –Decreased all-cause mortality with BMI ≥30
Increased all-cause mortality risk with BMI <21.9
BMI ≥30 kg/m2
HR 0.89 (0.81-0.99).042
BMI <21.9 kg/m2
HR 1.41 (1.15-1.71).001
BMI 21.9-24.9 kg/m2
HR 1.21 (0.99-1.46).057
BMI 25-29.9 kg/m2
HR 0.95 (0.78-1.16).61
BMI >29.9 kg/m2
HR 1.00Ref
43Ethnicity, gender, age, diabetes, years on HD, BMI, amputations, catheter access. hemoglobin, serum albumin, ESRD NetworkHispanics 58.5 (14.9)
Whites 63.6 (14.9)
Blacks 57.1 (14.8)		Hispanics 54
Whites
56
Blacks 50		Hispanic 12.8
Whites 46.8
Blacks 40.3		18,336 (7,723)All-cause mortality –Decreased all-cause mortality with higher average BMI
HR 0.97 (0.96-0.99)<.001
44Age, gender, smoking, HD duration, serum albumin, blood pressure, urea reduction ratio50.1 (1.1)71.5–12116 (116)All-cause mortality.023 (overall)CVD 26
Vascular 21
Infection 16
Sudden death 14
Tumor 7
Other 16		Increased all-cause mortality risk with BMI >23
BMI <16.9
RR 9.1 (0.8-45.5)
BMI 17-18.9
RR 1.0 (Reference)
BMI 19-20.9
RR 1.7 (0.4-9.0)
BMI 21-22.9
RR 2.9 (0.6-14.3)
BMI >23
RR 4.2 (1.1-16.8)
45Age, gender, race, BMI, undernourished, serum albumin, diabetes, coronary heart disease, left ventricular hypertrophy, peripheral vascular disease, pulmonary disease, smoking, ambulatory status, ESRD duration57.1 (16)50Blacks 37.153,607 (3,607)All-cause mortality –Decreased all-cause mortality with higher baseline lnBMI
Per 1 unit e.g., 3.7 versus 2.7
RR 0.65 (−).0002
46Adjusted but not clear how58 (15)56–2.1200 (190)All-cause mortality –Increase in survival with each unit of higher BMI
RR survival 1.14 (1.03-1.27).01
47Age, gender, race, diabetes, incident in 199864.2 (−)51Asian 2.7
Black 35
Native Am 1.7
White 58.9
Other 1.7		245,967 (−)All-cause mortality –Increased all-cause mortality with lower BMI <23.1
Decreased all-cause mortality with higher BMI >27.8
BMI <23.1 kg/m2
RR 1.19 (−)
BMI 23.2-27.8 kg/m2
RR 1.00 (−)<.0001
BMI >27.8 kg/m2
RR 0.84 (−)
9Age, gender, race, incidence year diabetes, BMI, serum albumin, creatinine, BUN, number of days from 1st dialysis placement date75.2 (−)49.3White 73.1
Black 23
Native Am 1
Asian Am 2.9		366,595 (66,595)All-cause mortality –Decreased all-cause mortality with baseline BMI >25
Increased all-cause mortality with baseline BMI <19
BMI 25-30
HR 0.865 (0.832-0.900)<.0001
BMI >30
HR 0.851 (0.808-0.896)<.0001
BMI <19
HR 1.268 (1.212-1.327)<.0001
48Demographic variables, comorbid conditions, Medicare insurance, functional status, serum albumin.64.8 (14.2)48White 66.6
Afr-Am 25.6
Other –		570,028 (70,028)All-cause mortality CVD 22.6
All cause 42.8		All-cause mortality
BMI, each 5 kg/m2 increase Decreased all-cause mortality with higher BMI (continuous data) and BMI 18.5-<25 (categorical data)
Increased all-cause mortality with BMI >30 (categorical data)
HR 0.95 (0.93-0.96)<.001
BMI 18.5-<25
BMI, each 5 kg/m2 increase
HR 0.87 (0.83-0.91)<.001
BMI 25-<30
BMI, each 5 kg/m2 increase
HR 0.94 (0.87-1.01)<.097
BMI ≥30
BMI, each 5 kg/m2 increase
HR 1.04 (1.01-1.07)<.021
Cardiovascular mortality
BMI, each 5 kg/m2 increase
HR 0.95 (0.94-0.97)<.001
BMI 18.5-<25 Cardiovascular mortality
BMI, each 5 kg/m2 increaseDecreased cardiovascular mortality with higher BMI (continuous data) and BMI 18.5-<25 (categorical data)
HR 0.91 (0.85-0.96)<.001
BMI 25-<30
BMI, each 5 kg/m2 increase
HR 0.90 (0.81-1.00).055
BMI ≥30
BMI, each 5 kg/m2 increase
HR 1.02 (0.98-1.07).317
49Demographic and clinical variables (lymphocytes, neutrophils, age, gender, diabetes, race, BMI, creatinine, albumin, potassium, calcium, phosphate, bicarbonate)59.9 (15.2)51White 48
Black 45
Other 7		125,661 (17,568)All-cause mortality –Decreased all-cause mortality risk with higher average BMI
HR 0.984 (0.979-0.989)<.001
50Age, sex, diabetes, smoking, blood pressure, HD duration, high flux dialyzers, hemoglobin, leukocytes, platelets, total protein, cholesterol, triglycerides, BUN, creatinine, potassium, Ca X P, PTH, ferritin, BMI, α-blockers, β-blockers, EPODied 70 (11)
Survived 62 (15)		Died 53
Survived 50.3		–2.5188 (188)All-cause mortality CVD 72
Infection 24
Tumor 4		Decreased all-cause mortality risk with higher baseline BMI
RR 0.89 (0.82-0.97)<.001
51Age, gender, HD duration, blood pressure, diabetes, serum albumin, CRP, HDL, BMI55 (11)59–5.3525 (525)Cardiovascular mortality CVD 37
Infection 22
Tumor 12
Liver cirrhosis 4
Cachexia 3
Ileus 2
Pulmonary embolism 1
Acute pancreatitis 1
Suicide 1
Other 18		No significant relationship with cardiovascular mortality
HR 0.993 (0.875-1.127).912
Randomised Controlled Study with Retrospective Pooling of the Data for Analysis of Mortality Risk (N = 1)
52Demographics, dialysis vintage, HEMO treatment group, comorbidity, bicarbonate, CRP, BMI, serum creatinine, albumin (RCT for dialysis type; data pooled for this analysis)57 (14)57Afr Am 67Up to 2.31,000 (1,000)All-cause mortality CVD 12Inverse association between serial measurements of BMI and all-cause and cardiovascular mortality
HR 0.93 (0.91-0.96)–
CVD mortality
HR 0.95 (0.91-0.99)–

Afr Am, African American; AGE, Advanced glycation end-products; ALP, Alkaline phosphatase; BMI, Body mass index; BUN, Blood urea nitrogen; Ca, Calcium; CI, Confidence intervals; CML, carboxymethyllysine; CVD, Cardiovascular disease; EPO, Erythropoietin; ESRD, End stage renal disease; fT3, triiodothyronine; GIT, gastrointestinal tract; HR, Hazards ratio; HD, Hemodialysis; HDL, High-density lipoprotein; hsCRP, High sensitivity C-reactive protein; iPTH, Intact parathyroid hormone; IL, Interleukin; Kt/V, Adequacy of dialysis; LDL, Low density lipoprotein; MBL, Mannose-binding lectin; nPCR, Normalised protein catabolic rate; P, Phosphate; RCT, randomized controlled trial; RR, Relative risk; SD, Standard deviation; SGA, Subjective global assessment; tMTHFR, methylenetetrahydrofolate-reductase.

∗Continuous data unless otherwise indicated.

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References 

  1. WHO: Prevention and Control of Noncommunicable Diseases: Implementation of the Global Strategy. Geneva, Switzerland: Report by the Secretariat, World Health Organization; 2008;
  2. Abrass CK. Overview: obesity: what does it have to do with kidney disease?. J Am Soc Nephrol. 2004;15:2768–2772
  3. Griffin KA, Kramer H, Bidani AK. Adverse renal consequences of obesity. Am J Physiol. 2008;294:F685–F696
  4. McGill JB, Furth S, Frederick J, et al. Obesity: growing impact on CKD. ACKD. 2006;13:323–324
  5. Wang Y, Chen X, Song Y, et al. Association between obesity and kidney disease: a systematic review and meta-analysis. Kidney Int. 2008;73:19
  6. Stengel B, Tarver-Carr ME, Powe N, et al. Lifestyle factors, obesity and the risk of chronic kidney disease. Epidemioloy. 2003;14:479–487
  7. Tozawa M, Iseki K, Iseki C, et al. Triglyceride, but not total cholesterol or low-density lipoprotein cholesterol levels, predict development of proteinuria. Kidney Int. 2002;62:1743–1749
  8. Abbott KC, Glanton CW, Trespalacios FC, et al. Body mass index, dialysis modality, and survival: analysis of the United States Renal Data System Dialysis Morbidity and Mortality Wave II Study. Kidney Int. 2004;65:597–605
  9. Xue JL, Dahl D, Ebben JP, et al. The association of initial hemodialysis access type with mortality outcomes in elderly Medicare ESRD patients. Am J Kidney Dis. 2003;42:1013–1019
  10. Dwyer JT, Larive B, Leung J, et al. Are nutritional status indicators associated with mortality in the Hemodialysis (HEMO) Study?. Kidney Int. 2005;68:1766–1776
  11. Satomura A, Endo M, Fujita T, et al. Serum mannose-binding lectin levels in maintenance hemodialysis patients: impact on all-cause mortality. Nephron Clin Pract. 2006;102:c93–c99
  12. WHO: Avoiding Heart Attacks and Strokes. Geneva, Switzerland: World Health Organization; 2005;
  13. National Kidney Foundation: K/DOQI Clinical Practice Guidelines for Managing Dyslipidemias in Chronic Kidney Disease. Available at: http://www.kidney.org/professionals/kdoqi/guidelines_lipids/index.htm2003;Accessed March 17, 2009
  14. Kasiske B, Cosio FG, Beto J, et al. Clinical practice guidelines for managing dyslipidemias in kidney transplant patients: a report from the Managing Dyslipidemias in Chronic Kidney Disease Work Group of the National Kidney Foundation Kidney Disease Outcomes Quality Initiative. Am J Transplant. 2004;4(Suppl 7):S13–S53
  15. Herselman M, Esau N, Kruger JM, et al. Relationship between serum protein and mortality in adults on long-term hemodialysis: exhaustive review and meta-analysis. Nutrition. 2010;26:10–32
  16. Gøtzsche PC, Harden A. Searching for non-randomised studies. NRSMG, 7th draft. Available at: www.cochrane.dk/nrsmg/docs/chap3.rtf2000;Accessed October 12, 2005
  17. Cochrane Renal Group: Renal Group Data Extraction Form 2005. Available at: http://www.cochrane-renal.org/docs/data_extraction_form.doc. Accessed November 27, 2008
  18. Khan KS, Ter Riet G, Glanville J, et al. Undertaking Systematic Reviews of research on Effectiveness. CRD's Guidance for those Carrying out or Commissioning Reviews. CRD Report Number 4. 2nd edn. York, University of York: York Publishing Services Ltd; 2001;
  19. STROBE Statement of essential items Version 3. Available at: http://www.strobe-statement.org/PDF%20hochladen/STROBE-Checklist-Version3.pdf. Accessed November 8, 2005
  20. Alderson P and Green S (eds): Cochrane Collaboration open learning material for reviewers, Version 1.1. 2002. Available at: http://www.cochrane-net.org/openlearning/. Accessed February 22, 2007
  21. Wells G, Shea B, O' Connell D, et al: The Newcastle-Ottawa Scale (NOS) for assessing the quality of non-randomised studies in meta-analyses. Available at: http://www.iri.ca/programs/ccu/oxford.htm. Accessed October 13, 2005
  22. Khan KS, Kunz R, Kleijnen J, et al. Systematic Reviews to Support Evidence-Based Medicine. London, UK: The Royal Society of Medicine Press Ltd; 2003;
  23. The COCHRANE COLLABORATION: Cochrane Handbook for Systematic Reviews of Interventions 4.2.5. Available at: http://www.cochrane-handbook.org/. Accessed October 13, 2005
  24. Hintze J. NCSS Statistical and Power Analysis Software Package. Kaysville, UT: Number Cruncher Statitical Systems; 2007;
  25. Bologa RM, Levine DM, Parker TS, et al. Interleukin-6 predicts hypoalbuminemia, hypocholesterolemia, and mortality in hemodialysis patients. Am J Kidney Dis. 1998;32:107–114
  26. De Mutsert R, Snijder MB, Van der Sman-de Beer F, et al. Association between body mass index and mortality is similar in the hemodialysis population and the general population at high age and equal duration of follow-up. J Am Soc Nephrol. 2007;18:967–974
  27. Hanson JA, Hulbert-Shearon TE, Ojo AO, et al. Prescription of twice-weekly hemodialysis in the USA. Am J Nephrol. 1999;19:625–633
  28. Leavey SF, McCullough K, Hecking E, et al. Body mass index and mortality in ‘healthier’ as compared with ‘sicker’ haemodialysis patients: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrol Dial Transplant. 2001;16:2386–2394
  29. Zimmermann J, Herrlinger S, Pruy A, et al. Inflammation enhances cardiovascular risk and mortality in hemodialysis patients. Kidney Int. 1999;55:648–658
  30. Fleischmann E, Teal N, Dudley J, et al. Influence of excess weight on mortality and hospital stay in 1346 hemodialysis patients. Kidney Int. 1999;55:1560–1567
  31. Joly D, Anglicheau D, Alberti C, et al. Octogenarians reaching end-stage renal disease: cohort study of decision-making and clinical outcomes. J Am Soc Nephrol. 2003;14:1012–1021
  32. Termorshuizen F, Dekker FW, van Manen JG, et al. Relative contribution of residual renal function and different measures of adequacy to survival in hemodialysis patients: an analysis of the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD)-2. J Am Soc Nephrol. 2004;15:1061–1070
  33. Chen KH, Lin-Tan DT, Huang WH, et al. Cardiothoracic ratio, malnutrition, inflammation, and two-year mortality in non-diabetic patients on maintenance hemodialysis. Kidney Blood Press Res. 2008;31:143–151
  34. Costa-Hong V, Jorgetti V, Gowdak LHW, et al. Parathyroidectomy reduces cardiovascular events and mortality in renal hyperparathyroidism. Surgery. 2007;142:699–703
  35. Morioka T, Emoto M, Tabata T, et al. Glycemic control is a predictor of survival for diabetic patients on hemodialysis. Diabetes Care. 2001;24:909–913
  36. Nascimento MM, Pecoits-Filho R, Qureshi AR, et al. The prognostic impact of fluctuating levels of C-reactive protein in Brazilian haemodialysis patients: a prospective study. Nephrol Dial Transplant. 2004;19:2803–2809
  37. Saudan P, Kossovsky M, Halabi G, et al. Quality of care and survival of haemodialysed patients in western Switzerland. Nephrol Dial Transplant. 2008;23:1975–1981
  38. Terrier N, Jaussent I, Dupuy AM, et al. Creatinine index and transthyretin as additive predictors of mortality in haemodialysis patients. Nephrol Dial Transplant. 2008;23:345–353
  39. Kalantar-Zadeh K, Kopple JD, Block G, et al. A malnutrition-inflammation score is correlated with morbidity and mortality in maintenance hemodialysis patients. Am J Kidney Dis. 2001;38:1251–1263
  40. Kimmel PL, Chawla LS, Amarasinghe A, et al. Anthropometric measures, cytokines and survival in haemodialysis patients. Nephrol Dial Transplant. 2003;18:326–332
  41. Mallamaci F, Zoccali C, Tripepi G, et al. Hyperhomocysteinemia predicts cardiovascular outcomes in hemodialysis patients. Kidney Int. 2002;61:609–614
  42. Nishizawa Y, Shoji T, Maekawa K, et al. Intima-media thickness of carotid artery predicts cardiovascular mortality in hemodialysis patients. Am J Kidney Dis. 2003;41(suppl 1):S76–S79
  43. Frankenfield DL, Rocco MV, Roman SH, et al. Survival advantage for adult Hispanic hemodialysis patients? Findings from the end-stage renal disease clinical performance measures project. J Am Soc Nephrol. 2003;14:180–186
  44. Kaizu Y, Tsunega Y, Yoneyama T, et al. Overweight as another nutritional risk factor for the long-term survival of non-diabetic hemodialysis patients. Clin Nephrol. 1998;50:44–50
  45. Leavey SF, Strawderman RL, Jones CA, et al. Simple nutritional indicators as independent predictors of mortality in hemodialysis patients. Am J Kidney Dis. 1998;31:997–1006
  46. Lorenzo V, Martin M, Rufino M, et al. High prevalence of overweight in a stable Spanish hemodialysis population: a cross sectional study. J Ren Nutr. 2003;13:52–59
  47. Port FK, Ashby VB, Dhingra RK, et al. Dialysis dose and body mass index are strongly associated with survival in hemodialysis patients. J Am Soc Nephrol. 2002;13:1061–1066
  48. Beddhu S, Pappas LM, Ramkumar N, et al. Effects of body size and body composition on survival in hemodialysis patients. J Am Soc Nephrol. 2003;14:2366–2372
  49. Reddan DN, Klassen PS, Szczech LA, et al. White blood cells as a novel mortality predictor in haemodialysis patients. Nephrol Dial Transplant. 2003;18:1167–1173
  50. Tepel M, Giet MV, Park A, et al. Association of calcium channel blockers and mortality in haemodialysis patients. Clin Sci (Lond). 2002;103:511–515
  51. Nishizawa Y, Shoji T, Kakiya R, et al. Non-high-density lipoprotein cholesterol (non-HDL-C) as a predictor of cardiovascular mortality in patients with end-stage renal disease. Kidney Int. 2003;84(suppl):S117–S120
  52. Beddhu S, Cheung AK, Larive B, et al. Inflammation and inverse associations of body mass index and serum creatinine with mortality in hemodialysis patients. J Ren Nutr. 2007;17:372–380
  53. In:  Lee RD,  Nieman DC editor. 3rd edn. Nutritional Assessment. Chap 6:London: McGraw Hill; 2003;
  54. Inoue S, Zimmet P, Caterson I, et al. The Asia-Pacific perspective: redefining obesity and its treatment. Geveva, Switzerland: World Health Organisation; 2000;22
  55. James PT, Leach R, Kalamara E, et al. The worldwide obesity epidemic. Obesity Res. 2001;9(suppl 4):2287–233
  56. Globalization, Diets and Noncommunicable Diseases. Geneva, Switzerland: World Health Organization; 2002;
  57. Allison DB, Fontaine KR, Manson JE, et al. Annual deaths attributable to obesity in the United States. JAMA. 1999;282:1530–1538
  58. Sturm R, Wells KB. Does obesity contribute as much to morbidity as poverty or smoking?. Public Health. 2001;115:229–235
  59. MacMahon SW, Wilcken DE, Macdonald GJ. The effect of weight reduction on left ventricular mass: a randomized controlled trial in young, overweight hypertensive patients. N Engl J Med. 1986;314:334–339
  60. Lavie CJ, Milani RV. Effects of cardiac rehabilitation, exercise training, and weight reduction on exercise capacity, coronary risk factors, behavioral characteristics, and quality of life in obese coronary patients. Am J Cardiol. 1997;79:397–401
  61. Szczech LA, Reddan DN, Klassen PS, et al. Interactions between dialysis-related volume exposures, nutritional surrogates and mortality among ESRD patients. Nephrol Dial Transplant. 2003;18:1585–1591
  62. Wigfield CH, Lindsey JD, Muñoz A, et al. Is extreme obesity a risk factor for cardiac surgery? An analysis of patients with a BMI ≥40. Eur J Cardiothorac Surg. 2006;29:434–440
  63. Pischon T, Sharma AM. Obesity as a risk factor in renal transplant patients. Nephrol Dial Transplant. 2001;16:14–17
  64. Johnson DW, Isbel NM, Brown AM, et al. The effect of obesity on renal transplant outcomes. Transplantation. 2002;74:675–681
  65. Horwich TB, Fonarow GC, Hamilton MA, et al. The relationship between obesity and mortality in patients with heart failure. J Am Coll Cardiol. 2001;38:789–795
  66. Mosterd A, Cost B, Hoes AW, et al. The prognosis of heart failure in the general population: the Rotterdam study. Eur Heart J. 2001;22:1318–1327
  67. Hall JA, French TK, Rasmusson KD, et al. The paradox of obesity in patients with heart failure. J Am Acad Nurse Pract. 2005;17:542–546
  68. Horwich TB, Fonarow GC. The impact of obesity on survival in patients with heart failure. Heart Fail Monit. 2002;3:8–14
  69. Lavie CJ, Milani RV. Obesity and cardiovascular disease: the Hippocrates paradox?. J Am Coll Cardiol. 2003;42:677–679
  70. Lavie CJ, Osman AF, Milani RV, et al. Body composition and prognosis in chronic systolic heart failure: the obesity paradox. Am J Cardiol. 2003;91:891–894
  71. Lissin LW, Gauri AJ, Froelicher VF, et al. The prognostic value of body mass index and standard exercise testing in male veterans with congestive heart failure. J Card Fail. 2002;8:206–215
  72. Mosterd A, Cost B, Hoes AW, et al. The prognosis of heart failure in the general population: the Rotterdam study. Eur Heart J. 2001;22:1318–1327
  73. Strandberg TE, Strandberg AY, Salomaa VV, et al. Explaining the obesity paradox: cardiovascular risk, weight change, and mortality during long-term follow-up in men. Eur Heart J. 2009;30:1720–1727
  74. Chazot C, Gassia J-P, Di Benedetto A, et al. Is there any survival advantage of obesity in Southern European haemodialysis patients?. Nephrol Dial Transplant. 2009;24:2871–2876
  75. Takata Y, Ansai T, Soh I, et al. Association between body mass index and mortality in an 80-year old population. J Am Geriatr Soc. 2007;55:913–917
  76. Diehr P, O'Meara ES, Fitzpatrick A, et al. Weight, mortality, years of healthy life, and active life expectancy in older adults. J Am Geriatr Soc. 2008;56:76–83
  77. McAuley P, Myers J, Abella J, et al. Body mass, fitness and survival in veteran patients: another obesity paradox?. Am J Med. 2007;120:518–524
  78. Kalantar-Zadeh K, Horwich TB, Oreopoulos A, et al. Risk factor paradox in wasting diseases. Curr Opin Clin Nutr Metab Care. 2007;10:433–442
  79. Corrada M, Kawas CH, Mozaffar F, et al. Association of body mass index and weight change with all-cause mortality in the elderly. Am J Epidemiol. 2006;163:938–949
  80. Marton KI, Sox HC, Krupp JR. Involuntary weight loss: diagnostic and prognostic significance. Ann Intern Med. 1981;95:568–574
  81. Rabinovitz M, Pitlik SD, Leifer M, et al. Unintentional weight loss. A retrospective analysis of 154 cases. Arch Intern Med. 1986;146:186–187
  82. Bilbao-Garay J, Barba R, Losa-Garcia JE, et al. Assessing clinical probability of organic disease in patients with involuntary weight loss: a simple score. Eur J Intern Med. 2002;13:240–245
  83. Locher L, Roth DL, Ritchie CS, et al. Body mass index, weight loss, and mortality in community-dwelling older adults. J Gerontol A Biol Sci Med Sci. 2007;62:1389–1392
  84. Wannamethee SG, Shaper AG, Lennon L. Reasons for intentional weight loss, unintentional weight loss, and mortality in older men. Arch Intern Med. 2005;165:1035–1040
  85. Williamson DF, Pamuk E, Thun M, et al. Prospective study of intentional weight loss and mortality in never-smoking overweight US white women aged 40-64 years. Am J Epidemiol. 1995;141:1128–1141
  86. Yaari S, Goldbourt U. Voluntary and involuntary weight loss: associations with long term mortality in 9228 middle-aged men and elderly men. Am J Epidemiol. 1998;148:546–555
  87. Williamson DF, Pamuk E, Thun M, et al. Prospective study of intentional weight loss and mortality in overweight white men aged 40-64 years. Am J Epidemiol. 1999;149:491–503
  88. French SA, Folsom AR, Jeffery RW, et al. Prospective study of intentional weight loss and mortality in older women: the Iowa Women's Health Study. Am J Epidemiol. 1999;149:504–514
  89. Gregg EW, Gerzoff RB, Thompson TJ, et al. Intentional weight loss and death in overweight and obese US adults 35 years of age and older. Ann Intern Med. 2003;138:383–389
  90. Williamson DF, Flanders D, Thompson TJ, et al. Intentional weight loss and mortality among overweight individuals with diabetes. Diabetes Care. 2000;23:1499–1504
  91. Wannamethee SG, Shaper AG, Walker M. Overweight and obesity and weight change in middle-aged men: impact on cardiovascular disease and diabetes. J Epidemiol Comm Health (in press)
  92. Field AE, Malspeis S, Willett WC. Weight cycling and mortality among middle-aged or older women. Arch Intern Med. 2009;169:881–886
  93. Wallace JI, Schwartz RS, LaCroix AZ, et al. Involuntary weight loss in older outpatients: incidence and clinical significance. J Am Geriatr Soc. 1995;43:329–337
  94. Schooling CM, Lam TH, Li ZB, et al. Obesity, physical activity, and mortality in a prospective Chinese elderly cohort. Arch Intern Med. 2006;166:1498–1504
  95. Morley JE, Thomas DR, Wilson MG. Cachexia: pathophysiology and clinical relevance. Am J Clin Nutr. 2006;83:735–743
  96. Stenvinkel P. Inflammatory and atherosclerotic interactions in the depleted uremic patient. Blood Purif. 2001;19:53–61
  97. Kalantar-Zadeh K, Abbott KC, Salahudeen AK, et al. Survival advantages of obesity in dialysis patients. Am J Clin Nutr. 2005;81:543–554
  98. Kaysen GA. The microinflammatory state in uremia: causes and potential consequences. J Am Soc Nephrol. 2001;12:1549–1557
  99. Mattern WD, Hak LJ, Lamanna RW, et al. Malnutrition, altered immune function, and the risk of infection in maintenance hemodialysis patients. Am J Kidney Dis. 1982;1:206–218
  100. Walrand S, Moreau K, Caldefie F, et al. Specific and nonspecific immune responses to fasting and refeeding differ in healthy young adult and elderly persons. Am J Clin Nutr. 2001;74:670–678
  101. Huang CC, Cheng KF, Wu HDI. Survival analysis: comparing peritoneal dialysis and hemodialysis in Taiwan. Perit Dial Int. 2008;28(suppl 3):S15–S20
  102. Van Dijk PCW, Jager KJ, de Charro F, et al. Renal replacement therapy in Europe: the results of a collaborative effort by the ERA-EDTA registry and six national or regional registries. Nephrol Dial Transplant. 2001;16:1120–1129
  103. Banerjee D, Ma JZ, Collins AJ, et al. Long-term survival of incident hemodialysis patients who are hospitalized for congestive heart failure, pulmonary edema, or fluid overload. Clin J Am Soc Nephrol. 2007;2:1186–1190
  104. Nigam A, Wright RS, Allison TG, et al. Excess weight at time of presentation of myocardial infarction is associated with lower initial mortality risks but higher long-term risks including recurrent re-infarction and cardiac death. Int J Cardiol. 2006;110:153–159
  105. Epstein BJ, Gums JG. Angiotensin receptor blockers versus ACE inhibitors: prevention of death and myocardial infarction in high-risk populations. Ann Pharmacother. 2005;39:470–480
  106. Galandi D, Schwarzer G, Antes G. The demise of the randomised controlled trial: bibliometric study of the German-language health care literature, 1948 to 2004. BMC Med Res Methodol. 2000;6:30
  107. In:  Higgins JPT,  Green S editor. Cochrane Handbook for Systematic Reviews of Interventions. West Sussex, UK: John Wiley & Sons Ltd; 2008;

PII: S1051-2276(10)00081-6

doi:10.1053/j.jrn.2010.03.010

Journal of Renal Nutrition
Volume 20, Issue 5 , Pages 281-292.e7, September 2010

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