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ORIGINAL RESEARCH COMMUNICATION |
1 From the Division of Nephrology, University of California San Francisco (KLJ, BY, and GMC); the San Francisco VA Medical Center, San Francisco (KLJ); and the Division of Nephrology, University of California, Davis (GAK)
2 The authors are responsible for the content of this article. The article does not represent government policy. 3 Supported by contract N01-DK-1-2450 from the National Institutes of Health, National Institute of Diabetes, Digestive and Kidney Diseases. 4 Address reprint requests to GM Chertow, Department of Medicine Research, University of California, San Francisco, UCSF Laurel Heights Suite 430, 3333 California Street, San Francisco, CA 94118-1211. E-mail: chertowg{at}medicine.ucsf.edu. 5 Address correspondence to KL Johansen, Nephrology Section, Box 111J, San Francisco VA Medical Center, 4150 Clement Street, San Francisco, CA 94121. E-mail: johanse{at}itsa.ucsf.edu.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Objective: We sought to clarify the relation between body size and outcomes among a large cohort of patients beginning dialysis.
Design: Data on 418 055 patients beginning dialysis between 1 April 1995 and 1 November 2000 were analyzed by using US Renal Data System data. BMI was divided into 8 categories in increments of 3 units, ranging from <19 to 
37, and the relation between survival and BMI was examined by using proportional hazards regression with adjustment for demographic, laboratory, and comorbidity data.
Results: High BMI was associated with increased survival in this cohort, even at extremely high BMI, after adjustment, and over a 2-y average follow-up time. This was true for whites, African Americans, and Hispanics but not for Asians. High BMI was also associated with a reduced risk of hospitalization and a lower rate of mortality in all mortality categories. Alternative estimates of adiposity, including the Benn index and estimated fat mass, yielded similar results, and adjustments for lean body mass did not substantially alter the findings.
Conclusions: High BMI is not associated with increased mortality among patients beginning dialysis. This finding does not appear to be a function of lean body mass and, although modified by certain patient characteristics, it is a robust finding.
Key Words: End-stage renal disease • dialysis • body mass index • adiposity • survival • outcomes
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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It is possible that greater body fat affords a survival advantage to patients on dialysis. However, reports of the association between adiposity and outcomes in dialysis patients have to date focused on BMI, an imperfect measure of adiposity, and have included relatively few categories of BMI (9, 11, 13). These characteristics limit the usefulness of available data. It is unclear whether the improved survival at higher BMI is a result of greater body fatness or of the increased muscle mass that generally accompanies obesity. In addition, the possibility that mild overweight or obesity may be advantageous, whereas greater degrees are associated with an increased hazard of death cannot be excluded. Finally, follow-up periods in such studies have generally been short, typically 1 y (8-10, 12), which makes it possible that high BMI could be a short-term advantage but a longer-term disadvantage.
Given the magnitude of the risks of obesity in the general population, it is important to clarify whether these risks apply to patients on dialysis, who have an overall cardiovascular risk at least 10 times greater. In addition, many other uncertainties about the relation between adiposity and survival among dialysis patients remain. Specifically, the interactions of race or ethnicity and modality with the BMI-survival relation have not been examined carefully. Associations have not been reported between BMI and specific causes of death or the likelihood of hospitalization. The current study was designed to address these uncertainties by using data from an inclusive cohort of patients beginning dialysis between April 1, 1995, and November 30, 2000, and followed for 1 y. We hypothesized that extremely high BMI would not be associated with increased survival time. In addition, if there were a survival advantage at higher BMI, we hypothesized it would be explained in part by the increased lean body mass (LBM) that usually accompanies high BMI.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Predictor variables
BMI was divided into the following categories: <19, 19–37 (in increments of 3 units), and 37. These categories were chosen to cover a broad range of BMIs and to conform approximately to World Health Organization classifications (15). Because BMI is an imperfect estimate of adiposity, we examined other estimates of body fatness for comparison. The Benn index (W/Hp) was calculated for males and females separately so that there would be no correlation between the Benn index value and height (16). Categories similar to BMI categories were created for analysis. Categories of estimated fat mass were also created using demographic information. LBM was estimated by using an ESRD-specific equation for total body water (V; 17):
 | (1) |
Estimated fat mass was calculated by subtracting estimated LBM from weight. Categories of estimated fat mass were generated to parallel the distribution obtained by using BMI and to facilitate comparisons with BMI results.
Sociodemographic predictor variables included age, sex, race (white, African American, or Asian), ethnicity (Hispanic or other), employment status (unemployed, employed full-time or part-time, homemaker, retired, student, or medical leave of absence), type of insurance (employer group health insurance, Medicaid, Medicare, Veterans Affairs coverage, other, or no insurance), and geographic region (as delineated by the ESRD Network; Internet: esrdnetworks.org/networks_defined.htm).
Initial dialysis modality (peritoneal or hemodialysis) and history of a kidney transplant were included in the models. Comorbid conditions included the following from the Medical Evidence form: congestive heart failure, ischemic heart disease, cardiac arrest, cardiac dysrhythmia, pericarditis, cerebrovascular disease, peripheral vascular disease, hypertension, diabetes, chronic obstructive pulmonary disease, tobacco use, cancer, alcohol dependence, drug dependence, inability to ambulate, and inability to transfer. HIV-positive status and AIDS diagnosis were not included in the analysis because the relatively new inclusion of these items on CMS Form 2728 meant that there were few data. Laboratory data included serum creatinine concentration, serum albumin concentration, and hematocrit.
Statistical analysis
Baseline patient characteristics by BMI category were compared by using analysis of variance for continuous variables and the Cochran-Armitage Trend test for dichotomous variables. Cox proportional hazards regression analysis was used to determine whether survival was associated with categories of BMI. Survival data were available through November 30, 2001, which allowed a minimum of 1 y after the initiation of dialysis for all patients. The reference BMI category for all analyses was 22–25. This category was chosen as the reference because it is within the normal or ideal range of BMI set forth by the World Health Organization (15), because it was the modal category in this population, and because it allowed for comparison with several categories of higher BMI. The models were adjusted for all of the predictor variables as well as for the interactions between BMI and dialysis modality and history of kidney transplant. Similar models were constructed using categories of the Benn index and estimated body fat mass.
Missing data for serum creatinine (n = 4171, or 1% of the subjects) were replaced with the mean. Missing values for hematocrit were replaced with 3 times the hemoglobin concentration if hemoglobin was available. Values for which there were no replacements (n = 26 909, or 6.3% of the subjects) were coded as missing. Because serum albumin data were missing for a substantial number of patients (n = 95 558, or 22.5% of the population), albumin was entered into the models as a categorical variable by using quartiles of serum albumin and a category for "missing" (<2.8, 2.8 to <3.2, 3.2 to <3.7, 3.7 g/dL, and missing). Separate models were constructed for each race or ethnic group of the population and by sex within each group.
To broaden the evaluation of the association of adiposity and survival, proportional hazards models were also generated for men and women by using the Benn index and estimated body fat categories as predictor variables. Separate models of the BMI-survival association were generated for whites, African Americans, Asians, and Hispanics.
To test whether the BMI-survival association had a U-shaped distribution and to determine whether the shape of the association was different for different racial groups, for men and women, for patients with and without diabetes, and for different concentrations of serum creatinine (a proxy for muscle mass), a model that used BMI as a continuous variable was constructed. A squared BMI term and multiplicative interaction terms were included in this model. Because of concern that adjusting for diabetes might represent overadjustment if diabetes were a cause of increased mortality in patients with high BMI, analyses were repeated without adjustment for diabetes.
Separate models were performed for the 4 most common categories of the cause of death (cardiac, infectious, vascular, and other). For these analyses, survival time was censored at the time of death from any other cause. Additional unadjusted analysis of the annual rate of mortality due to each cause of death by BMI category was performed to ensure that censoring did not distort the relation between death due to various causes and BMI. The relation between BMI and hospitalization was also examined. A model stratified by whether initiation of dialysis was preceded by a failed kidney transplant was constructed, and the effect of the dialysis modality was explored in patients beginning dialysis without prior transplantation.
Separate models with and without adjustment for muscle mass by using serum creatinine concentration or creatinine index (24-h creatinine production adjusted for body weight) and with and without adjustment for estimated LBM were constructed to determine the extent to which the association between high BMI and survival might be related to increased muscle mass in patients with high BMI. SAS software (version 8.2; SAS Institute, Cary, NC) was used for all analyses, and P 
0.01 was considered significant.
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. There were significant differences in most patient characteristics by BMI category, owing to the large sample size. Many of the differences in patient characteristics by BMI category were also clinically significant, defined here as a relative difference of 25% of the lowest BMI category or an absolute difference of 10% between the highest and lowest BMI categories. These characteristics include male sex, Asian race, history of myocardial infarction, cardiac dysrhythmia, pericarditis, prior cerebrovascular accident, peripheral vascular disease, chronic obstructive pulmonary disease, tobacco use, cancer, alcohol dependence, and drug dependence, all of which decreased across categories, and diabetes, hypertension, and African American race, which increased across BMI categories. It is notable that differences in variables potentially related to nutritional status, including serum creatinine and albumin concentrations, did not vary greatly across groups, although differences across categories were statistically significant. Only those in the lowest BMI category (<19) had lower concentrations of both serum creatinine and albumin than did the other groups.
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. As expected, low BMI was associated with increased mortality. For the group as a whole, higher BMI was associated with increased survival time, and this positive association extended even to extreme obesity and persisted after adjustment for all of the predictor variables. The Benn index for men was weight/height1.939 and that for women was weight/height1.818, so that there was almost no residual correlation between the index and height (r2 = 0.0001 for men and women). Analyses using the Benn index or estimated body fat mass did not significantly alter the shape of the estimated adiposity-survival relation.
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). The analysis was repeated after excluding Pacific Islanders from the Asian category, which resulted in a considerably more variable relation between BMI and survival in the higher BMI categories.
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). However, the upturn was small, and the survival advantage at extremely high BMIs persisted. The interaction term for African American race was significant (P < 0.0001), and there was a stronger association between BMI and survival in African Americans than in the other racial or ethnic groups (see Figure 2). As expected, there was a significant (P < 0.0001) interaction between Asian race and BMI. The association between BMI and survival was somewhat attenuated in women (P < 0.0001). There was no significant interaction between diabetes and BMI or serum creatinine and BMI. Exclusion of diabetes from the model did not materially alter the relation between BMI and survival.
As for overall mortality, the hazard ratios for cardiac death, vascular death, and other-cause death were all significantly lower in the high BMI groups (data not shown). However, the risk of infectious death was not significantly lower in women with extremely high BMI than it was in the groups with normal BMI. There were not enough deaths in the liver or gastrointestinal death categories to allow discernment of a clear association between BMI and mortality due to these causes. The unadjusted annual mortality rate from each cause by BMI category is shown in Table 2. It is evident that, even without adjustment for other risk factors, the risk of death declined with higher BMI. Analysis of hospitalization yielded reductions in risk in the higher BMI categories that paralleled the reductions in mortality observed and shown in the figures (data not shown).
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. Persons beginning dialysis treatment for the second time, after a failed transplant, had a lower rate of mortality than did those beginning dialysis treatment for the first time. However, the association between BMI and survival in these 2 populations was different. Patients at both extremes of BMI had a higher risk of death than did those with BMI between 22 and 37. The association between high BMI and increased survival was also less evident among patients with new-onset ESRD who were beginning peritoneal dialysis.
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). Because the serum creatinine could be influenced by level of renal function in this group of patients beginning dialysis, a further analysis was performed after adjustment for 24-h creatinine production adjusted for body weight (creatinine index) in patients for whom creatinine clearance and serum creatinine data were available (n = 123 660). Separate analyses of the data from the entire cohort with and without adjustment for estimated LBM were also performed. Adjustment for serum creatinine had little effect on the association between BMI and survival, whereas adjustment for creatinine index tended to strengthen the association (Figure 4). Patients with creatinine clearance data were older and more likely to be white, female, and insured. Patients with creatinine clearance data were also more likely to have comorbidities than were patients with no recorded creatinine clearance (data not shown). Adjustment for estimated LBM attenuated the association between BMI and survival somewhat, but survival was still higher among patients in the higher BMI categories.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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An observational study design of this sort is not optimal for making causal inferences, but some interpretation of the findings is possible. Specifically, these analyses provide little evidence to support our hypothesis that increased survival in obese persons is related to greater muscle mass. Table 1 shows little association between serum creatinine concentrations and BMI, and the relation between BMI and survival was not abrogated by adjustment for serum creatinine, creatinine index, or estimated LBM (Figure 4). Likewise, the lack of strong association between BMI and albumin and the persistence of an association between BMI and survival after adjustment for albumin suggest that inflammation is not likely to be an important factor in establishing the association between BMI and mortality. Rather, it seems possible that the true association is with body fatness and that additional fat is actually protective. In general, end-stage renal disease represents a catabolic and inflammatory state (19-21). Patients undergoing dialysis appear to waste over time (22), and it may be advantageous for a person to begin dialysis treatment at a higher level of adiposity. Patients with greater body fat mass at dialysis initiation may be protected by greater energy reserves in the face of general wasting or may be less prone to wasting processes than are patients with less body fat. The Kidney Disease Outcomes Quality Initiative guidelines have recognized the potential negative effect of predialysis weight loss, and they currently recommend that dialysis be initiated in patients with a low glomerular filtration rate and significant weight loss associated with uremia, even in the absence of other indications (23). Our data tend to support this recommendation even for persons who are above their ideal body weight. Our data do not address the issue of the effect of weight loss or weight gain on survival in the dialysis population, but they do suggest the need for caution and further study before making a strong recommendation of weight loss for those patients awaiting kidney transplantation. Observational studies have suggested that short-term and long-term graft survival rates after kidney transplantation are marginally better in patients with lower BMIs (4, 6, 7). These studies have formed the basis for widespread recommendations that obese patients hoping to be considered for transplantation should lose weight. However, the association between obesity and poorer outcomes after transplantation is not a universal finding (24-26). Whereas peritransplant morbidities and complications associated with obesity are relevant, we do not know how these morbidities might be balanced by the effects of losing weight while awaiting transplant. With an ever-stronger body of literature now supporting the benefit of increased body mass in patients on dialysis and with ever-longer transplant waiting times, the recommendation for weight loss must be considered more carefully.
Creatinine clearance data were missing for most of these patients and were not used in the main analysis (although the main analyses were adjusted for serum creatinine concentration). Patients with creatinine clearance data had significantly different characteristics than did patients for whom this information was not available, and the former group may have had better access to medical care during moderate and advanced stages of chronic kidney disease. However, the relation between BMI and survival in patients with creatinine production data did not differ significantly from that in the whole population, as can be seen by comparing the results for the whole population after adjustmentfor serum creatinine (Figure 2) with the results for the subset with creatinine clearance data (Figure 4). Beddhu et al (14) recently made an important contribution to the literature in this area by using creatinine production as a surrogate for muscle mass and by comparing the relation between BMI 25 and survival among patients in the lowest quartile of creatinine production with that among patients with higher BMIs and higher levels of creatinine production. However, our conclusions do not support those of Beddhu et al, who stated that the survival advantage conferred by high BMI in dialysis patients is limited to patients with normal or high muscle mass. Using many more categories of BMI, we show here that, overall, high BMI is associated with a survival advantage after adjustment for creatinine production or LBM. The reasons for the discrepancy are not certain but increased power in our analysis and differences in data handling may account for the differences. We eliminated patients below the 1st percentile for height in the general population (7.4% compared with an expected 1%) because they were disproportionately represented in the high BMI categories, and in many cases, an error in recording or transcription was likely. However, even with these patients included in the analysis, higher BMI was associated with improved survival (data not shown).
In agreement with Wong et al (18), we noted that Asian patients did not enjoy a survival advantage at higher BMIs. Rather, Asian patients showed a trend toward higher mortality in the higher BMI categories. This trend was not as evident when Pacific Islanders were excluded from the Asian category, which suggests that this subgroup accounted in part for the higher mortality at higher BMI. However, even without the Pacific Islanders included, Asians clearly did not have a positive association between BMI and survival, as did the other racial and ethnic groups. Further study of more specific subgroups of Asian patients could help to clarify this issue. The lessening of the apparent benefit of adiposity in patients who start peritoneal dialysis is consistent with the findings of Snyder et al (27), who observed an improved survival among peritoneal dialysis patients in the first year and a progressive flattening of this relation with time.
The observational nature of this study is a limitation that prevents us from determining whether the improved survival at high BMI is a result of the extra body fat or of some other factor associated with high BMI. Analyses to test alternative interpretations were restricted by the quality of data collected on Form 2728. Thus, adiposity was estimated on the basis of height and weight only. However, an advantage to the approach of this study is that multiple methods of estimation of adiposity were used, and they yielded consistent results across a wide spectrum of categories. The limited data set also precluded examination of the extent to which higher BMI was associated with more sensitive markers of inflammation or nutritional status. However, the large sample size and relative completeness of the available data are strengths of the study. The large sample size allowed us to examine the BMI-survival relation in specific racial and ethnic groups and in other subsets of the population for whom little information was previously available. Although the clinical and demographic data were missing in relatively few cases, it is known that the CMS Form 2728 is insensitive to capture of some comorbid conditions (28) and that it provides no information about the severity of the conditions. Thus, our analyses could not perfectly adjust for the effects of comorbid conditions. However, analysis without any adjustment for comorbidity (Table 2) showed that there were fewer absolute deaths in the high BMI categories. Furthermore, a similar pattern was observed for the 4 major categories of the cause of death in this population.
This study shows that even extremely high BMI is associated with increased survival for most patients beginning dialysis. The relation between BMI and mortality is nonlinear and complex in that there are significant differences in the degree of association between high BMI and survival among different subpopulations, such as racial groups, the sexes, transplant recipients, and patients undergoing different dialysis modalities. At present, our findings suggest that patients who begin dialysis with high BMI should not be advised to lose weight to improve survival. However, there may be other important reasons to encourage weight loss, such as improved quality of life, and the associations between BMI and quality of life in this population remain to be studied.
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ACKNOWLEDGMENTS |
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REFERENCES |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
) and women (
) by category of BMI (A), Benn index (B), and estimated fat mass (C). Data were analyzed by using proportional hazards regression. Error bars represent 95% CIs. The interaction between sex and BMI was significant.
. B: Data are shown for the entire cohort (n = 418 055). Results without adjustment for estimated lean body mass,