SEARCH

SEARCH BY CITATION

Keywords:

  • BMI;
  • dialysis;
  • kidney transplantation;
  • obesity;
  • patient survival;
  • waitlisting

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Research suggests that end-stage renal disease patients with elevated body mass index (BMI) have superior outcomes on dialysis. In contrast, low and high BMI patients represent the highest risk cohorts for kidney transplant recipients. The important question remains concerning how to manage transplant candidates given the potentially incommensurate impact of BMI by treatment modality. We conducted a retrospective analysis of waitlisted and transplanted patients in the United States from 1990 to 2003. We constructed Cox models to evaluate the effect of BMI on mortality of waitlisted candidates and identified risk factors for rapid weight change. We then assessed the impact of weight change during waitlisting on transplant outcomes. Decline in BMI on the waiting list was not protective for posttransplant mortality or graft loss across BMI strata. Substantial weight loss pretransplantation was associated with rapid gain posttransplantation. The highest risk for death was among listed patients with low BMI (13–20 kg/m2, adjusted hazard ratio = 1.47, p < 0.01). Approximately one-third of candidates had a change in BMI category prior to transplantation. While observed declines in BMI may be volitional or markers of disease processes, there is no evidence that candidates have improved transplant outcomes attributable to weight loss. Prospective trials are needed to evaluate the efficacy of weight loss protocols for candidates of kidney transplantation.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Body mass index (BMI) is an independent predictor of cardiovascular mortality and morbidity in the general population and is associated with chronic renal insufficiency and chronic renal failure (1–5). The independent effects of BMI on outcomes for end-stage renal disease (ESRD) patients have been examined in a variety of settings. In general, high BMI is reported to be protective for dialysis patients (6–10). This ‘reverse epidemiological’ finding has been explained by various mechanisms including a more stable hemodynamic status in obese patients, protein-energy malnutrition in low BMI patients, and competing risks for patients prior to ESRD onset (11). Weight loss in patients on dialysis has additionally been demonstrated to be a risk factor for patient death independent of patients' baseline BMI (9).

In contrast, large retrospective studies indicate that renal transplant recipients with both low and high BMI have diminished graft and patient survival following transplantation (12,13). Obesity in transplant recipients has also been associated with increased complication rates, costs, length of stay and delayed graft function following the procedure (12,14–16). Moreover, weight gain following transplantation has been demonstrated to be associated with inferior long-term outcomes (17,18). Despite the relatively superior prognosis for obese patients as compared to nonobese patients on dialysis and a relatively inferior prognosis following transplantation, kidney transplantation still significantly increases life expectancy in this portion of the ESRD population (19,20). However, due to the relatively poor short- and long-term outcomes of obese patients following transplantation, certain centers exclude patients based on BMI and others advocate aggressive weight loss prior to transplantation (21,22). Due to the potential hazards that appear to be associated with lower BMI and weight loss on dialysis, the important question remains whether weight loss protocols for transplant candidates are in the best interest of patients and whether weight loss prior to transplantation improves outcomes following transplantation.

In the present study, we examined the impact of BMI from the time of candidate placement on the waiting list on outcomes prior to and following transplantation. We examined patient survival for waitlisted patients based on BMI classification. We described absolute weight changes prior to transplantation and associated risk factors for increased rates of weight change. We additionally analyzed the association between weight change prior to transplantation with patient and graft survival following transplantation. Finally, we described the association of weight change posttransplant with changes prior to transplantation.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

We examined data for renal transplant candidates that were waitlisted for a first solitary transplant with available data documented in the United States Renal Data System (USRDS) aged 18–70 between 1990 and 2003. The primary variable of interest was patient BMI, calculated as (weight [kg]/(height [m])2). We used the following categorization for BMI for the analysis and throughout the manuscript: underweight (13–<20 kg/m2), normal (20–<25 kg/m2), overweight (25–<30 kg/m2), obese (30–<35 kg/m2) and morbidly obese (35+ kg/m2). Of note, these category definitions have been used in prior research, but also vary slightly from current National Heart Lung Blood Institute (NHLBI) criteria (23). For the purpose of this analysis, we utilized BMI at the time of candidate wait-listing, at the time of transplantation and at 6 and 12 months posttransplantation. Files containing height and weight were utilized specifically from each of these time points and merged with a unique patient identifier available in the database. We assessed the impact of both absolute changes in BMI and rates of change in BMI. Absolute changes in BMI during wait-listing were calculated as a percentage of waitlisted BMI from the time of wait-listing to transplant. Rates of change were calculated as the percentage difference in BMI during listing divided by the time to transplantation (in years). Cases in which weight parameters were unknown were excluded from models. In cases of missing height parameters at a given time point, we utilized levels from other time periods or in cases of discrepancies we averaged values. For recipients with multiple transplant episodes over the study period, only data from the initial transplant and applicable waitlisting period were incorporated in the analysis.

Cox proportional hazard models were utilized for the outcome of death for waitlisted patients with follow-up time censored at transplantation. Independent variables that were incorporated in these models included patient race/ethnicity, primary cause of ESRD, age, gender and dialysis treatment at the time of listing. To verify findings in the presence of additional potential confounding factors, we also extracted patient information from the time of ESRD onset from the medical evidence (2728) form including: serum albumin, employment status, history of tobacco use, insurance status and presence/history of comorbid conditions. This additional information was only available in a subset of the study population (as data were available after 1994).

We also utilized multivariate Cox proportional hazard models to assess recipient outcomes from the time of transplantation for outcomes of patient and graft survival. These models were formed based on absolute weight changes and by the rate of weight changes. Posttransplant models were adjusted for donor age, donor race, HLA-mismatching and donor type (living or deceased donor) along with recipient age, race, gender, time of pretransplant dialysis and primary cause of ESRD.

General linear models were utilized to assess factors associated with the rate of weight change. For these models, outliers were excluded for the dependent variable and models were stratified by waitlisted BMI (<25 kg/m2, 25–30 kg/m2 and >30 kg/m2). A last follow-up date of September 30, 2004, representing 90 days before the last available event data, was used as an additional censor in all survival models to allow for full data acquisition. Means for continuous variables were compared with analysis of variance models and unadjusted comparisons of categorical frequencies were conducted with chi-square tests. All analyses were conducted in SAS (v.9.1.3, Cary, NC).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Patient characteristics

The study included 162 284 solitary kidney transplant candidates between 1990 and 2003 of which 124 713 patients received a transplant. An additional 72 679 patients had indications of waitlisting, but did not have adequate information to calculate BMI. The demographics of this cohort are displayed in Table 1 by a five-level categorization of candidate BMI. Younger candidates more frequently had lower BMI and candidates 50–60 years of age were most likely to be obese. Asian candidates were over twice as likely to be underweight as compared to Caucasians. African American and Native American candidates were most likely to be obese or morbidly obese. Candidates with type-II diabetes were the most likely to be obese or morbidly obese at the time of listing. Females were more than twice as likely to be underweight but also were more frequently morbidly obese.

Table 1.  Adult candidate demographics by body mass index level at the time of wait-listing for solitary kidney transplant
Candidate characteristicLevelSample sizeWaitlisted BMI (kg/cm2)
13–<2020–<2525–<3030–<3535+
  1. 1Study population includes candidates 18–70 years of age at the time of waitlisting; other or missing levels not shown. BMI = body mass index; ESRD = end-stage renal disease.

Age118–<40 (%)52 85315.144.024.710.5 5.7
40–<50 (%)43 305 8.236.831.815.5 7.8
50–<60 (%)41 798 5.832.035.618.7 8.0
60–<70 (%)24 328 5.632.539.417.3 5.2
Race/Ethnicity1Caucasian (%)88 442 9.538.631.414.3 6.2
African American (%)44 036 7.733.032.417.8 9.1
Asian (%)735721.047.721.9 6.8 2.6
Hispanic (%)19 177 8.737.234.014.4 5.8
Native American (%)1773 5.627.235.922.8 8.5
Primary cause of ESRD1Type-I diabetes (%)17 693 9.147.228.810.6 4.4
Type-II diabetes (%)28 753 4.127.336.122.110.4
Glomerulonephritis (%)34 53710.837.530.114.4 7.1
Hypertension (%)31 619 7.935.433.815.9 7.0
GenderMale (%)96 169 6.438.035.214.7 5.7
Female (%)66 11513.936.126.315.3 8.4
Dialysis time at waitlisting>3 years (%)61 696 9.137.332.114.9 6.6
1–3 years44 718 8.735.331.916.4 7.6
<1 year31 57812.640.628.412.5 5.8
Preemptive (%)24 292 7.736.133.815.6 6.8
All candidates(%)162 284 9.437.231.615.0 6.8

The distribution of BMI at the time of waitlisting changed substantially over the study period. As displayed in Figure 1A, the proportion of candidates that were obese at the time of listing has more than doubled over the study period (12.3–25.8%). In a similar fashion, the proportion of underweight candidates decreased from 14.4% to 6.5%. Figure 1B displays the corresponding proportion of recipient BMI at the time of transplantation over the study period. The percentage of transplant recipients who were morbidly obese has increased from 3.5% in 1990 to 8.4% in 2003.

image

Figure 1. (A) Transplant candidate body mass indices by year of listing. (B) Transplant recipient body mass indices by year of transplant.

Download figure to PowerPoint

Waitlisted candidate survival

The results of the multivariate Cox proportional hazard model for patient survival following candidate listing censored at transplantation are displayed in Table 2. Relative to overweight candidates, underweight and normal weight candidates had a significantly increased risk for death during wait-listing. Other significant risk factors for candidate death during listing included increased age and prelisting dialysis. There was significant variation in the relative hazard for death associated with primary cause of ESRD and non-Caucasians had significantly lower risk for death.

Table 2.  Adjusted COX proportional hazard model for candidate death on the transplant waiting list
Explanatory variable (reference level)LevelAdjusted hazard ratio95% Confidence interval
  1. 1‘Other’ or missing levels not displayed.

  2. ESRD = end-stage renal disease; GN = glomerulonephritis.

Waitlisted body mass index13–<201.471.39–1.56
20–<251.131.09–1.18
(25–<30)30–<350.990.95–1.04
35+1.030.97–1.10
Age at wait-listing40–<501.621.54–1.70
50–<602.322.21–2.44
(18–<40)60–<703.082.92–3.25
Race/Ethnicity (Caucasian)1Asian0.530.49–0.58
African American0.700.67–0.73
Hispanic0.660.63–0.70
Native American0.680.60–0.78
Primary cause of ESRD (GN)1Type-I diabetes2.872.70–3.05
Type-II diabetes2.472.34–2.61
Interstitial nephritis1.221.11–1.34
Secondary GN1.701.56–1.85
Hypertension1.371.30–1.46
Neoplasms/Tumors2.081.78–2.44
Cystic/Congenital disease0.840.77–0.92
Gender (Male)Female0.980.95–1.01
On dialysis at waitlisting (No)Yes2.001.87–2.13

For patients with available information, we reconstructed the model adjusted for history of comorbidities from the medical evidence (2728) form. In this model, underweight (adjusted hazard ratio [AHR]= 1.42, 95% confidence interval [CI] 1.33–1.50) and normal weight (AHR = 1.11, 95% CI 1.07–1.15) patients also had a significant hazard for death. In addition to the significant risk factors in Table 2, low albumin levels, candidates with a history of smoking, ischemic heart disease, chronic obstructive pulmonary disease or congestive heart failure also demonstrated an increased hazard for death in this model. Primary insurance type also had a significant association with candidate outcome; candidates with private payers demonstrated the lowest risk for death. Additionally, candidates who were employed at the time of ESRD had a lower mortality risk.

By restricting the analysis to candidates on dialysis at the time of wait-listing, we noted similar estimates for the association of BMI and death; candidates with low BMI had an elevated hazard relative to overweight candidates (AHR = 1.44, 95% CI 1.35–1.53). For diabetic patients, the effect was exacerbated for low BMI (AHR = 1.56, 95% CI 1.41–1.73). By excluding candidates who received a living donation over the study period, we observed slightly different estimates including morbidly obese patients having a significantly reduced relative hazard of death as compared to moderately high BMI candidates (AHR = 0.90, 95% CI 0.84–0.96) and a similar hazard for underweight candidates (AHR = 1.35, 95% CI 1.27–1.43).

Absolute change in BMI between wait-listing and transplant

Table 3 illustrates the relative proportion of candidate BMI category with the corresponding transplant BMI category. Approximately one-third of transplant recipients had a change in BMI category during listing varying by baseline level. Among normal weight candidates, 7.2% of candidates became underweight by the time of transplant, while over 18% became overweight or obese. Among overweight candidates, almost 16% had a reduction in BMI category, while approximately 14% became obese by the time of transplant. Almost a quarter of candidates who were obese at the time of listing reduced their BMI category by the time of transplant and over 30% of morbidly obese had a reduction in BMI category.

Table 3.  Distribution of waitlisted candidate BMI by transplant recipient BMI
 Transplant recipient BMI (kg/m2)
13–<2020–<2525–<3030–<3535+
  1. n = 101 983.

  2. Percentages indicate the distribution of recipient body mass index (BMI) category within candidate listing BMI category (i.e. row percentages total 100%), shaded values indicate the percentage of candidates that had the same body mass index (BMI) category at the time of transplantation.

Candidate waitlisted BMI (kg/m2)13–<2064.529.93.31.21.1
20–<257.274.416.61.50.4
25–<300.915.070.112.71.3
30–<350.93.119.564.911.7
35+2.22.66.419.969.1
Total9.536.932.015.26.4

Factors associated with rate of BMI change

Median (and 25th and 75th percentiles) waiting times for transplant recipients were 12.6 months (4.8, 26.4) and 6.5 months (3.6, 13.2) from deceased and living donor sources, respectively. Table 4 displays the results of general linear models for the rate of BMI change stratified by waitlisted BMI level. For candidates with low and normal BMI (<25 kg/m2), rates of change significantly varied by primary cause of ESRD, gender, race and dialysis utilization at wait-listing. For overweight and obese patients, primary cause of ESRD, gender and dialysis utilization at wait-listing were significantly associated with rates of change. In particular, females, type-II diabetics and patients on dialysis had consistently increased rates of weight gain prior to transplantation across all BMI strata.

Table 4.  General linear models for factors associated with rate of weight change during waitlisted period
FactorWaitlisted BMI
Underweight or normal weight (<25 kg/m2)Overweight (25–30 kg/m2)Obese (>30 kg/m2)
Parameter estimatep-Value*Parameter estimatep-Value*Parameter estimatep-Value*
  1. n = 89 001.

  2. Rate of weight change is BMI difference between waitlisting and transplant divided by the time to transplant (in years), outliers (rates exceeding 30% per year) excluded from the models.

  3. BMI = body mass index; PDGN = primary cause of ESRD; GN = glomerulonephritis.

  4. *p-Value represents test for effect of level equal to zero.

Intercept−0.150.31−1.34<0.01−2.53<0.01
PDGN: Type-I diabetes−0.250.100.140.460.340.21
PDGN: Type-II diabetes0.250.130.50<0.010.71<0.01
PDGN: Secondary GN−0.390.05−0.240.43−1.010.01
PDGN: Interstitial nephritis0.520.02−0.260.34−0.030.93
PDGN: Hypertension0.160.270.230.150.030.89
PDGN: Cystic disease0.50<0.010.360.06−0.080.75
PDGN: Neoplasms0.180.68−0.380.53−0.850.28
PDGN: Miscellaneous conditions0.290.040.180.32−0.290.23
PDGN: GN0Ref0Ref0Ref
Female0.84<0.010.75<0.010.76<0.01
Male0Ref0Ref0Ref
Age: 60–700.300.050.200.230.220.31
Age: 50–600.250.040.110.430.200.27
Age: 40–500.160.130.140.300.040.81
Age: 18–400Ref0Ref0Ref
Race: Asian0.180.33−0.610.06−0.220.67
Race: African American0.100.39−0.130.31−0.350.82
Race: Hispanic0.40<0.01−0.030.85−0.080.73
Race: Native American0.850.10−0.550.25−0.100.86
Race: Other0.510.19−0.510.33−1.490.05
Race: Caucasian0Ref0Ref0Ref
On dialysis at listing1.33<0.011.48<0.011.30<0.01
Not on dialysis at listing0Ref0Ref0Ref

Outcomes after transplantation

The relative hazards for patient death following transplantation were elevated for both low and high BMI at the time of transplant. Relative to overweight patients, underweight (AHR = 1.14, 95% CI 1.08–1.20), obese (AHR = 1.08, 95% CI 1.03–1.13) and morbidly obese (AHR = 1.28, 95% CI 1.20–1.37) patients had an increased risk for death following transplantation. There was no significant difference in the hazard for death among recipients classified as normal weight relative to overweight recipients. Figures 2A–C depicts the adjusted relative hazards for overall graft loss by absolute weight changes stratified by BMI at waitlisting. For recipients from each stratum, there was no significant association with absolute weight change prior to transplantation with subsequent graft loss following transplantation. The analogous models for patient death revealed comparable nonsignificant associations.

image

Figure 2. (A) Adjusted hazard ratios for overall graft loss for obese candidates (BMI > 30 kg/m2) by pretransplant BMI change. (B) Adjusted hazard ratios for overall graft loss for overweight candidates (BMI 25–30 kg/m2) by pretransplant BMI change. (C) Adjusted hazard ratios for overall graft loss for normal and underweight candidates (BMI < 25 kg/m2) by pretransplant BMI change.

Download figure to PowerPoint

Table 5 displays the association between categorized rates of weight change with the relative hazard for graft loss stratified by waitlisted BMI. As indicated in the table, there was a significant association of graft loss with more rapid weight changes (either positive or negative) in nonobese patients. These relationships were confirmed when using the rate change as a continuous variable; there was a significant quadratic relationship in both groups. However, among obese candidates, neither the categorical nor the continuous variable for rate of weight change was significantly associated with overall graft loss.

Table 5.  Adjusted cox proportional hazard for overall graft loss associated with rate of BMI change during waitlisted period
Rate of ChangeWaitlisted BMI
Underweight or normal weight (<25 kg/m2)Overweight (25–30 kg/m2)Obese (>30 kg/m2)
nHazard ratio95% CInHazard ratio95% CInHazard ratio95% CI
  1. Rate of change calculated as percentage change divided by time to transplant (in years). Models adjusted for recipient primary diagnosis, gender, age, race, time of pretransplant dialysis, donor age, donor race and number of HLA-mismatches. Reference group is patients with −4 to +4% rate of change during listing.

  2. BMI = body mass index.

>12%91531.131.09–1.1841281.151.08–1.2119451.070.96–1.16
+8 to 12%26131.060.99–1.1414051.060.97–1.167021.040.92–1.19
+4 to 8%44790.930.88–0.9825151.020.95–1.1013211.030.93–1.13
−4 to +4%24 377Ref16 901Ref10 691Ref
−8 to −4%25231.071.00–1.1422041.020.95–1.1018581.040.95–1.13
−12 to −8%13391.050.96–1.1512221.040.95–1.159851.010.90–1.13
<−12%47071.071.02–1.1337481.101.04–1.1731671.000.93–1.08

Weight changes posttransplantation demonstrated an inverse relationship with absolute changes pretransplant (p < 0.01). As displayed in Figure 3, transplant recipients who had more than 12% of weight loss during listing exhibited the largest gains in the first 6 months posttransplantation. Alternatively, recipients who had the greatest weight increase during listing had a slight decrease in the first 6 months posttransplantation on average. Differences in weight changes were consistent and continued to increase to 1 year posttransplantation for recipients with a minimum of 1-year graft survival relative to changes pretransplantation. Recipients with the largest weight decrease during listing also had the highest increase in BMI in the first year posttransplantation (an average increase of 4.7 kg/m2). Among the recipients who lost more than 12% of weight during listing, 40% returned to a BMI at or above their waitlisted BMI by 1-year posttransplantation. Conversely, recipients with the highest weight increase pretransplant had only a slight increase in the first year posttransplantation (an average increase of 0.2 kg/m2).

image

Figure 3. Average change in BMI at 6 months posttransplantation by change in BMI pretransplantation, mean changes and standard errors displayed as text in the vertical bars.

Download figure to PowerPoint

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

The principal finding of our analysis is that there is no evidence that weight loss during candidate listing improves long-term outcomes following transplantation. In addition, rapid weight changes have a negative association with graft survival in nonobese patients. We also conclude that among the selected wait-list population, death rates are highest among patients with low BMI, analogous to the general ESRD population. Contrary to the general ESRD population, however, in the selected dialysis population on the waiting list, obesity did not confer an additional protective effect relative to an overweight BMI. Weight changes prior to transplantation were often transitory, as patients with large declines in BMI pretransplant often have significant weight gain following transplantation. Cumulatively, the evidence suggests that candidates who undergo significant weight loss during listing may be at higher risk for death prior to transplantation, receive little benefit from weight loss following transplantation, and weight that is lost is often rapidly regained. However, the important unanswered question from this study is the degree to which weight loss occurrences are volitional or rather a manifestation of disease processes, particularly in the obese cohort. These results are important as transplant centers concerned with potential increase in costs, complications and reduced long-term outcomes associated with obese recipients may advocate aggressive weight-loss protocols for patients prior to listing or transplantation. Prospective trials are needed to reconcile these findings and determine the efficacy of weight management strategies and evaluate whether weight-loss protocols are beneficial even at the potential expense of increased exposure to dialysis.

The rate of ESRD onset has continued to escalate in the United States influenced by an aging population, increased rates of hypertension and diabetes and more sophisticated and early diagnosis of chronic renal failure (24). In addition, increased BMI has been implicated as an independent cause of ESRD (25). This pattern is consistent in the population of waitlisted candidates and transplant recipients. The increased rate of obese candidates and recipients may also be attributable to the evolved perception that these patients represent a viable recipient cohort with acceptable long-term outcomes (26–29). However, given the growing shortage of available kidneys and the relatively reduced graft survival for obese recipients, controversy remains regarding the appropriate management and potential candidacy of this portion of the ESRD population. Abbott et al. have demonstrated that obese patients have decreased rates of listing for transplantation as compared to moderate BMI patients despite similar survival rates (6). The notion that BMI is a modifiable risk factor likely renders it different from other risk factors such as age and comorbidities. In this respect, access to transplantation may be obstructed to obese patients due to the presence of a risk that may be actively reduced in lieu of other factors that may be of higher consequence, but are not under the control of the patient. This issue is particularly critical for kidney transplant candidates as active management of patients that extends waiting time may inevitably be counterproductive considering the associated pretransplant mortality risk and detrimental posttransplant effects of increased dialysis time (30,31).

Paramount to our findings is that there is not an observable benefit of weight loss during waitlisting for improved patient and graft survival following the transplant procedure. Among obese candidates, for whom weight management is generally the most pertinent, there was not any association between weight changes and transplant outcomes. This may suggest that BMI at waitlisting is a sufficient prognostic indicator for long-term outcomes posttransplantation regardless of subsequent weight change in these patients. In fact it is reasonable to hypothesize that an elevated BMI is often a symptom of a wide variety of comorbidities that cannot be significantly altered by losing weight. Much of the end-organ damage caused by obesity is certainly not expected to be reversible in a short time after weight loss. Among nonobese patients, there is an association between both rapid weight increases and decreases with inferior transplant outcomes likely due to the fact that rapid weight changes in a normal weight dialysis population are expressions of the occurrence of additional complicating disease processes.

Despite the lack of evidence from our analysis that weight loss may improve transplant outcomes, there are significant limitations affecting the interpretation of our findings. One of the clear caveats for examining BMI from registry data is the absence of information regarding the exact nature of the weight alterations and more specific indications of nutritional status. In particular, we are not able to distinguish weight alterations that are a result of general lifestyle and dietary changes from cases which represent changes in fluid status, malnutrition, extremity amputation or are a manifestation of progressive disease. In this regard, the effect of decreased weight that is purposeful and potentially beneficial may be diluted by weight loss that is a reflection of illness. Additionally, we did not investigate changes based on dialysis modality which have been shown to have differential impact on the obese ESRD population (32–34). This analysis also did not explore the association of weight changes on short-term complications and costs which may also significantly motivate weight-loss protocols. Alternatively, our results concerning the impact of weight changes may be conservative given the fact that we only have secondary collections of BMI for candidates who reach transplant. As a result, the exclusion of marginal patients who died prior to receiving a transplant may underestimate the deleterious ramifications of weight loss in our analysis.

Our results do indicate that weight changes that occur during listing may be more common than generally appreciated. Approximately one-third of patients arrive at transplantation at a BMI classification different from that at the time of wait-listing. One of the growing realities in the management of transplant candidates is that patients may often have drastically different presentation at the time of the transplant procedure than at the time of listing. Our results highlight the potential importance of interim evaluations, indicate that particular segments of the ESRD population may be at heightened risk for condition changes, and suggest that further investigation is warranted to evaluate whether weight changes have different implications in certain patient groups.

The association in the dialysis population of obesity with decreased mortality has been shown repeatedly (6–10). In this analysis, we demonstrate a similar finding in a healthier subset of the dialysis population, those accepted for wait-listing for transplant. A portion of this association may be driven by the possibility that the population of patients with normal and low BMI is more likely to contain patients with low BMI secondary to fatal disease processes. It is also possible that instead of an advantage a high BMI constitutes a lack of relative disadvantage in patients on dialysis. In the general population and transplant population, a high BMI constitutes a significant risk factor for chronic renal failure progression and in return, renal failure progression constitutes a risk for cardiac mortality. In that sense, faster renal failure progression catalyzes the survival disadvantage in patients with high BMI. Contrariwise, on dialysis, patients are generally without renal function and the disadvantage of faster renal function decline in higher BMI patients is void. Therefore, obese dialysis patients are more similar to their nonobese counterparts with respect to cardiac mortality as all patients experience the same devastating effect of a lack of renal function. Additionally, a high BMI correlates with the presence of hypertension and elevated C-reactive protein levels in the general population; however, in dialysis patients the frequency of hypertension is nearly universal as is evidence for some degree of inflammation (35–37). Hence, if the risk of death for an elevated BMI is in part driven by its role in mediating hypertension or inflammation, this effect might no longer be significant in the dialysis population.

The paradoxical relationship between high BMI levels and improved patient survival has increasingly been suspected in other medical contexts. Research suggests that obesity is associated with improved survival after the development of heart failure, but as with dialysis patients the mechanisms of this association are at least partially unknown (38–41). Controversy also exists regarding the role of weight reduction and patient survival among heart failure patients (42,43). Moreover, there are recent reports debating the effect of BMI in the fields of geriatrics, nutrition and cardiology in addition to nephrology (44–50). Central to the debate in these fields are whether there is a strictly associative relationship between BMI and survival mediated by unobserved variables and whether BMI is a gross measure of fat content that requires more specific categorization regarding the type of body mass. Another possible interpretation of our findings is that any volitional weight loss that does occur among obese patients is likely to be a reduction in subcutaneous fat content rather than visceral fat which has been more strongly implicated in risk for development of insulin resistance, diabetes and atherosclerosis (51–53). Our results provide further evidence that the association of BMI with patient outcomes is complex, context dependent and that further prospective evaluation is necessary to inform management strategies and treatment modalities.

In summary, weight management of renal transplant candidates is a difficult challenge with unique considerations. Our findings indicate that among ESRD patients medically evaluated and cleared for the transplant procedure, overweight and obese patients have improved survival over underweight and normal weight patients while on dialysis. Furthermore, weight loss among candidates is not associated with improved survival after transplantation across BMI groups. Finally, significant weight loss appears to be transitory in nature as rapid weight gain following transplantation is most common among these patients. It is conceivable that healthy lifestyle and dietary choices can achieve weight loss without increasing risks for patients on dialysis and ultimately improve outcomes after transplantation, and this hypothesis requires prospective clinical assessment. However, it is also important to recognize that even well-monitored, nutritionally balanced weight-loss protocols take considerable time, and that increased time on dialysis is one of the strongest risk factors for patient death. Indiscriminate recommendations for weight loss without any specific guidance could potentially be dangerous to dialysis patients awaiting transplantation and there is no evidence to suggest an improvement in long-term outcomes for those patients who ultimately reach transplantation.

Acknowledgment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the U.S. government.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References
  • 1
    Ejerblad E, Fored CM, Lindblad P, Fryzek J, McLaughlin JK, Nyren O. Obesity and risk for chronic renal failure. J Am Soc Nephrol 2006; 17: 16951702.
  • 2
    Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW Jr. Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med 1999; 341: 10971105.
  • 3
    Allison DB, Fontaine KR, Manson JE, Stevens J, VanItallie TB. Annual deaths attributable to obesity in the United States. JAMA 1999; 282: 15301538.
  • 4
    Manson JE, Willett WC, Stampfer MJ et al. Body weight and mortality among women. N Engl J Med 1995; 333: 677685.
  • 5
    Stevens J, Plankey MW, Williamson DF et al. The body mass index-mortality relationship in white and African American women. Obes Res 1998; 6: 268277.
  • 6
    Abbott KC, Glanton CW, Agodoa LY. Body mass index and enrollment on the renal transplant waiting list in the United States. J Nephrol 2003; 16: 4048.
  • 7
    Fung F, Sherrard DJ, Gillen DL et al. Increased risk for cardiovascular mortality among malnourished end-stage renal disease patients. Am J Kidney Dis 2002; 40: 307314.
  • 8
    Kakiya R, Shoji T, Tsujimoto Y et al. Body fat mass and lean mass as predictors of survival in hemodialysis patients. Kidney Int 2006; 70: 549556.
  • 9
    Kalantar-Zadeh K, Kopple JD, Kilpatrick RD et al. Association of morbid obesity and weight change over time with cardiovascular survival in hemodialysis population. Am J Kidney Dis 2005; 46: 489500.
  • 10
    Wiesholzer M, Harm F, Schuster K et al. Initial body mass indexes have contrary effects on change in body weight and mortality of patients on maintenance hemodialysis treatment. J Ren Nutr 2003; 13: 174185.
  • 11
    Kalantar-Zadeh K. Causes and consequences of the reverse epidemiology of body mass index in dialysis patients. J Ren Nutr 2005; 15: 142147.
  • 12
    Gore JL, Pham PT, Danovitch GM et al. Obesity and outcome following renal transplantation. Am J Transplant 2006; 6: 357363.
  • 13
    Meier-Kriesche HU, Vaghela M, Thambuganipalle R, Friedman G, Jacobs M, Kaplan B. The effect of body mass index on long-term renal allograft survival. Transplantation 1999; 68: 12941297.
  • 14
    Johnson DW, Isbel NM, Brown AM et al. The effect of obesity on renal transplant outcomes. Transplantation 2002; 74: 675681.
  • 15
    Drafts HH, Anjum MR, Wynn JJ, Mulloy LL, Bowley JN, Humphries AL. The impact of pre-transplant obesity on renal transplant outcomes. Clin Transplant 1997; 11(5 Pt 2): 493496.
  • 16
    Armstrong KA, Campbell SB, Hawley CM, Nicol DL, Johnson DW, Isbel NM. Obesity is associated with worsening cardiovascular risk factor profiles and proteinuria progression in renal transplant recipients. Am J Transplant 2005; 5: 27102718.
  • 17
    Ducloux D, Kazory A, Simula-Faivre D, Chalopin JM. One-year post-transplant weight gain is a risk factor for graft loss. Am J Transplant 2005; 5: 29222928.
  • 18
    El Agroudy AE, Wafa EW, Gheith OE, Shehab el-Dein AB, Ghoneim MA. Weight gain after renal transplantation is a risk factor for patient and graft outcome. Transplantation 2004; 77: 13811385.
  • 19
    Glanton CW, Kao TC, Cruess D, Agodoa LY, Abbott KC. Impact of renal transplantation on survival in end-stage renal disease patients with elevated body mass index. Kidney Int 2003; 63: 647653.
  • 20
    Pelletier SJ, Maraschio MA, Schaubel DE et al. Survival benefit of kidney and liver transplantation for obese patients on the waiting list. Clin Transpl 2003; 7788.
  • 21
    Holley JL, Monaghan J, Byer B, Bronsther O. An examination of the renal transplant evaluation process focusing on cost and the reasons for patient exclusion. Am J Kidney Dis 1998; 32: 567574.
  • 22
    Modlin CS, Flechner SM, Goormastic M et al. Should obese patients lose weight before receiving a kidney transplant? Transplantation 1997; 64: 599604.
  • 23
    Classification of Overweight and Obesity by BMI. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults—executive summary. Available at http://www.nhlbi.nih.gov/guidelines/obesity/ob_tbl2.htm. Accessed August 20, 2006.
  • 24
    Kramer HJ, Saranathan A, Luke A et al. Increasing body mass index and obesity in the incident ESRD population. J Am Soc Nephrol 2006; 17: 14531459.
  • 25
    Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS. Body mass index and risk for end-stage renal disease. Ann Intern Med 2006; 144: 2128.
  • 26
    Howard RJ, Thai VB, Patton PR et al. Obesity does not portend a bad outcome for kidney transplant recipients. Transplantation 2002; 73: 5355.
  • 27
    Marks WH, Florence LS, Chapman PH, Precht AF, Perkinson DT. Morbid obesity is not a contraindication to kidney transplantation. Am J Surg 2004; 187: 635638.
  • 28
    Massarweh NN, Clayton JL, Mangum CA, Florman SS, Slakey DP. High body mass index and short- and long-term renal allograft survival in adults. Transplantation 2005; 80: 14301434.
  • 29
    Bennett WM, McEvoy KM, Henell KR, Valente JF, Douzdjian V. Morbid obesity does not preclude successful renal transplantation. Clin Transplant 2004; 18: 8993.
  • 30
    Meier-Kriesche HU, Port FK, Ojo AO et al. Effect of waiting time on renal transplant outcome. Kidney Int 2000; 58: 13111317.
  • 31
    Meier-Kriesche HU, Kaplan B. Waiting time on dialysis as the strongest modifiable risk factor for renal transplant outcomes—a paired donor kidney analysis. Transplantation 2002; 74: 13771381.
  • 32
    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: 597605.
  • 33
    Ganesh SK, Hulbert-Shearon T, Port FK, Eagle K, Stack AG. Mortality differences by dialysis modality among incident ESRD patients with and without coronary artery disease. J Am Soc Nephrol 2003; 14: 415424.
  • 34
    McDonald SP, Collins JF, Johnson DW. Obesity is associated with worse peritoneal dialysis outcomes in the Australia and New Zealand patient populations. J Am Soc Nephrol 2003; 14: 28942901.
  • 35
    Kahraman S, Yilmaz R, Akinci D et al. U-shaped association of body mass index with inflammation and atherosclerosis in hemodialysis patients. J Ren Nutr 2005; 15: 377386.
  • 36
    Ramkumar N, Cheung AK, Pappas LM, Roberts WL, Beddhu S. Association of obesity with inflammation in chronic kidney disease: A cross-sectional study. J Ren Nutr 2004; 14: 201207.
  • 37
    Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C-reactive protein levels in overweight and obese adults. JAMA 1999; 282: 21312135.
  • 38
    Curtis JP, Selter JG, Wang Y et al. The obesity paradox: Body mass index and outcomes in patients with heart failure. Arch Intern Med 2005; 165: 5561.
  • 39
    Gustafsson F, Kragelund CB, Torp-Pedersen C et al. Effect of obesity and being overweight on long-term mortality in congestive heart failure: Influence of left ventricular systolic function. Eur Heart J 2005; 26: 5864.
  • 40
    Hall JA, French TK, Rasmusson KD et al. The paradox of obesity in patients with heart failure. J Am Acad Nurse Pract 2005; 17: 542546.
  • 41
    Kalantar-Zadeh K, Block G, Horwich T, Fonarow GC. Reverse epidemiology of conventional cardiovascular risk factors in patients with chronic heart failure. J Am Coll Cardiol 2004; 43: 14391444.
  • 42
    Horwich TB, Fonarow GC, Hamilton MA, MacLellan WR, Woo MA, Tillisch JH. The relationship between obesity and mortality in patients with heart failure. J Am Coll Cardiol 2001; 38: 789795.
  • 43
    Lavie CJ, Milani R, Mehra MR, Ventura HO, Messerli FH. Obesity, weight reduction and survival in heart failure. J Am Coll Cardiol 2002; 39: 15631564.
  • 44
    Allison DB, Zannolli R, Faith MS et al. Weight loss increases and fat loss decreases all-cause mortality rate: Results from two independent cohort studies. Int J Obes Relat Metab Disord 1999; 23: 603611.
  • 45
    Beddhu S. The body mass index paradox and an obesity, inflammation, and atherosclerosis syndrome in chronic kidney disease. Semin Dial 2004; 17: 229232.
  • 46
    Bender R, Jockel KH, Trautner C, Spraul M, Berger M. Effect of age on excess mortality in obesity. JAMA 1999; 281: 14981504.
  • 47
    Grabowski DC, Ellis JE. High body mass index does not predict mortality in older people: Analysis of the longitudinal study of aging. J Am Geriatr Soc 2001; 49: 968979.
  • 48
    Gruberg L, Weissman NJ, Waksman R et al. The impact of obesity on the short-term and long-term outcomes after percutaneous coronary intervention: The obesity paradox? J Am Coll Cardiol 2002; 39: 578584.
  • 49
    Johansen KL, Young B, Kaysen GA, Chertow GM. Association of body size with outcomes among patients beginning dialysis. Am J Clin Nutr 2004; 80: 324332.
  • 50
    Nishizawa Y, Shoji T, Ishimura E. Body composition and cardiovascular risk in hemodialysis patients. J Ren Nutr 2006; 16: 241244.
  • 51
    Cefalu WT, Wang ZQ, Werbel S et al. Contribution of visceral fat mass to the insulin resistance of aging. Metabolism 1995; 44: 954959.
  • 52
    Kopelman PG. Obesity as a medical problem. Nature 2000; 404: 635643.
  • 53
    Shimokata H, Tobin JD, Muller DC, Elahi D, Coon PJ, Andres R. Studies in the distribution of body fat: I. Effects of age, sex, and obesity. J Gerontol 1989; 44: M66M73.