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American Journal of Kidney Diseases

Recipient Obesity and Kidney Transplant Outcomes: A Mate-Kidney Analysis

Published:April 16, 2021DOI:https://doi.org/10.1053/j.ajkd.2021.02.332

      Rationale & Objective

      The impact of extreme recipient obesity on long-term kidney transplant outcomes has been controversial. This study sought to evaluate the association of various levels of recipient obesity on kidney transplantation outcomes by comparing mate-kidney recipient pairs to address possible confounding effects of donor characteristics on posttransplant outcomes.

      Study Design

      Nationwide observational cohort study using mate-kidney models.

      Setting & Participants

      In analysis based on the Organ Procurement and Transplant Network/United Network of Organ Sharing database, 44,560 adult recipients of first-time deceased-donor kidney transplants from 2001 through 2016 were paired by donor.

      Predictors

      Recipient body mass index (BMI) categorized as 18-25 (n = 12,446), >25-30 (n = 15,477), >30-35 (n = 11,144; obese), and >35 (n = 5,493; extreme obesity) kg/m2.

      Outcomes

      Outcomes included patient survival, graft survival, death-censored graft survival, delayed graft function (DGF), and hospital length of stay.

      Analytical Approach

      Conditional logistic regression and stratified proportional hazards models were used to compare outcomes as odds ratios and hazard ratios (HRs), adjusted for recipient and transplant factors, using recipients with a BMI >35 kg/m2 as a reference.

      Results

      At a median follow-up of 3.9 years, adjusted odds ratios for DGF were 0.42 (95% CI, 0.36-0.48), 0.55 (95% CI, 0.48-0.62), and 0.73 (95% CI, 0.64-0.83) for BMI 18-25, >25-30, and >30-35 kg/m2, respectively (P < 0.001 for all). Death-censored graft failure was less frequent for BMI ≤25 and >25-30 kg/m2 (HRs of 0.66 [95% CI, 0.59-0.74] and 0.79 [95% CI, 0.70-0.88], respectively; P < 0.001 for both), but not for BMI >30-35 kg/m2 (HR, 0.91 [95% CI, 0.81-1.02]; P = 0.09). Length of stay and patient survival did not differ by recipient BMI.

      Limitations

      Observational study with limited detail regarding potential confounders.

      Conclusions

      Despite an increased risk of DGF likely unrelated to donor organ quality, long-term transplant outcomes among recipients with a BMI >35 kg/m2 are similar to those among recipients with a BMI >30-35 kg/m2, supporting a flexible approach to kidney transplantation candidacy in candidates with extreme obesity.

      Graphical abstract

      Index Words

      Patients with kidney disease, like all other Americans, struggle with obesity. Patients with obesity have improved survival after kidney transplantation compared with continued maintenance dialysis. Many transplant centers limit the eligibility of patients with body mass index (BMI) >35 kg/m2 for kidney transplantation because of safety concerns. We examined patient and transplant outcomes among recipients with BMI >35 kg/m2 versus recipients with BMI 18-25 (ie, nonobese), >25-30, and >30-35 kg/m2. Neither patient survival nor hospital length of stay differed across BMI categories. Delayed graft function was more common when BMI was >35 kg/m2. Death-censored graft failure was greater among the groups with BMI >30 kg/m2 compared with the nonobese groups. Our results suggest that strict BMI criteria for kidney transplantation may deserve reconsideration.
      Editorial, p. 484
      Obesity has become more common in the United States, and prospective kidney transplant recipients have become increasingly obese. An analysis of the Scientific Registry of Transplant Recipients found that body mass index (BMI) exceeded 30 kg/m2 in 40% of patients who underwent kidney transplantation before 2000 but in 60% after 2009.
      • Kwan J.M.
      • Hajjiri Z.
      • Metwally A.
      • Finn P.W.
      • Perkins D.L.
      Effect of the obesity epidemic on kidney transplantation: Obesity is independent of diabetes as a risk factor for adverse renal transplant outcomes.
      The impact of recipient obesity on long-term kidney transplant outcomes has been controversial. A recent meta-analysis found no significant mortality risk associated with recipient BMI ≥30 kg/m2 but found higher likelihoods of delayed graft function (DGF) and graft failure.
      • Hill C.J.
      • Courtney A.E.
      • Cardwell C.R.
      • et al.
      Recipient obesity and outcomes after kidney transplantation; a systematic review and meta-analysis.
      Nonetheless, kidney transplantation conferred a survival benefit upon patients with obesity compared with continued maintenance dialysis, similar to observations in patients without obesity.
      • Gill J.S.
      • Lan J.
      • Dong J.
      • et al.
      The survival benefit of kidney transplantation in obese patients.
      There are intuitively reasonable mechanisms by which recipient obesity might negatively affect transplant outcomes. Obesity is associated with more type 2 diabetes, hypertension, and cardiovascular disease, all of which are disadvantageous for patients. Obesity is also associated with hyperfiltration, leading to proteinuria and reduced kidney function.
      • Chagnac A.
      • Herman M.
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      Obesity induced glomerular hyperfiltration: its involvement in the pathogenesis of tubular sodium reabsorption.
      ,
      • Ducloux D.
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      One-year post-transplant weight gain is a risk factor for graft loss.
      Kidney injury may be promoted by inflammatory mediators released from excess adipose tissue. Obesity also increases the risk of perioperative complications.
      • Lynch R.J.
      • Ranney D.N.
      • Shijie C.
      • Lee D.S.
      • Samala N.
      • Englesbe M.J.
      Obesity, surgical site infection, and outcome following renal transplantation.
      • Lentine K.L.
      • Delos Santos R.
      • Axelrod D.
      • Schnitzler M.A.
      • Brennan D.C.
      • Tuttle-Newhall J.E.
      Obesity and kidney transplant candidates: how big is too big for transplantation?.
      • Cannon R.M.
      • Jones C.M.
      • Hughes M.G.
      • Eng M.
      • Marvin M.R.
      The impact of recipient obesity on outcomes after renal transplantation.
      Obesity-related changes in metabolism affect the bioavailability of immunosuppressive agents and consequently increase the risks of immunologic injury to the allograft.
      • Khwaja A.
      • El-Nahas M.
      Transplantation in the obese: separating myth from reality.
      Most transplant centers have established BMI thresholds beyond which candidates are no longer eligible for waitlisting, but there is considerable center-to-center variation in these BMI thresholds.
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      • Ikizler T.A.
      • Mallamaci F.
      • Zoccali C.
      Obesity and nephrology: results of a knowledge and practice pattern survey.
      Donor organ quality is a major determinant of allograft function and long-term transplant survival.
      • Dare A.J.
      • Pettigrew G.J.
      • Saeb-Parsy K.
      Preoperative assessment of the deceased-donor kidney: from macroscopic appearance to molecular biomarkers.
      • Philosophe B.
      • Malat G.E.
      • Soundararajan S.
      • et al.
      Validation of the Maryland Aggregate Pathology Index (MAPI), a pre-implantation scoring system that predicts graft outcome.
      • Sulikowski T.
      • Tejchman K.
      • Ziętek Z.
      • et al.
      Histopathologic evaluation of pretransplantation biopsy as a factor influencing graft function after kidney transplantation in 3-year observation.
      • Munivenkatappa R.B.
      • Schweitzer E.J.
      • Papadimitriou J.C.
      • et al.
      The Maryland aggregate pathology index: a deceased donor kidney biopsy scoring system for predicting graft failure.
      Donor issues may confound studies designed to evaluate the impact of recipient obesity on kidney transplant outcomes. Investigators may try to adjust for different donor variables, but important factors such as hemodynamic instability during organ procurement, use of vasoactive medications, and donor kidney histology are not readily ascertained.
      • Anglicheau D.
      • Loupy A.
      • Lefaucheur C.
      A simple clinico-histopathological composite scoring system is highly predictive of graft outcomes in marginal donors.
      ,
      • Lopes J.A.
      • Moreso F.
      • Riera L.
      Evaluation of pre-implantation kidney biopsies: comparison of Banff criteria to a morphometric approach.
      We sought to examine the relationship between recipient body size and transplant outcomes using an approach that reduces the confounding effects of donor variables by comparing transplant outcomes of the recipients of mate kidneys from a common deceased donor. Analysis of recipients as mate pairs controls for most donor factors aside from right/left donor laterality and cold ischemia time. Such data are inherently clustered, and appropriate statistical methods must be employed to obtain accurate estimates of the effects and variability of recipient- and transplant-related factors.

      Methods

      Study Population

      The study protocol was approved by the institutional review boards of the Allegheny Health System and the University of Chicago and performed in adherence to the Declaration of Helsinki, with a waiver of informed consent for the use of deidentified data. Using the Organ Procurement and Transplantation Network/United Network for Organ Sharing database, we identified pairs of first-time, kidney-alone recipients from deceased donors who received their transplants from January 2001 through December 2016, a cohort chosen to reflect contemporary immunosuppression and management. We restricted our analysis to adult patients who received perioperative antibody induction followed by maintenance therapy with tacrolimus and mycophenolic acid for whom information on BMI was available. BMI was calculated using the BMI_CALC variable, and the most recently updated value available before transplantation was used. Recipient groups were categorized based on BMI of 18-25, >25-30, >30-35 (obese), and >35 kg/m2 (extreme obesity; reference group). Outcomes among BMI groups were compared using mate-kidney models to control for donor factors.

      Statistical Analysis

      Clinical characteristics were reported descriptively for all 4 BMI groups and the entire study population. In addition, paired analyses were performed for 2 subsets of patients who shared a common donor but were discordant for BMI, with discordance defined as (1) BMI ≤30 kg/m2 versus BMI >30 kg/m2 or (2) BMI ≤35 kg/m2 versus BMI >35 kg/m2. Paired t tests, signed-rank tests, McNemar tests, and Bowker tests were used as appropriate to compare paired recipient subsets.
      Outcomes of interest included DGF (defined as dialysis within the first week after transplantation), hospital length of stay (LOS) for the transplant admission, and times to events of graft failure, death-censored graft failure, and death. Times to event were calculated from the date of kidney transplantation until repeat transplantation, return to dialysis, death, or December 31, 2016.
      Stratified Cox proportional hazards models and cause-specific hazards models were used to obtain hazard ratios (HRs) for patient death, graft failure, and death-censored graft failure among paired recipients. Conditional logistic regression models were fit to obtain odds ratios for DGF. Generalized linear models with repeated subjects were used to examine adjusted LOS. Models employed robust covariance matrix estimators to account for nonindependence between donor pairs. All models were adjusted for recipient factors including age, sex, race, year of transplant, time undergoing dialysis, time on waitlist, preemptive transplant, cause of kidney failure, diabetes, peripheral vascular disease, cytomegalovirus (CMV), antibody status for hepatitis B virus (HBV) and hepatitis C virus (HCV), panel-reactive antibody (a measure of recipient sensitization), and private insurance, as well as transplant factors (induction type, steroid maintenance, kidney on pump, distance from donor center, cold ischemia time, ABO [blood type] match, HLA mismatch, HLA-DR locus mismatch). Laterality of the allograft was also included in the models. Data were missing for <10% of observations for all variables except for panel-reactive antibody, for which there were 6 variables from which imputation could be performed. Missing data were handled by multiple imputation using the Markov chain Monte Carlo method, with generation of 50 imputation data sets. P values of 0.05 were considered to be statistically significant. Results are expressed as odds ratios and HRs with 95% CIs and P values, with values <1.00 indicating lower risk of the outcome compared with the reference group.
      We performed exploratory analyses using conditional logistic regression to examine acute rejection and graft failure during the first 1 year posttransplant. We also segregated patients with BMI ≥40 kg/m2 as a reference group to evaluate the impact, if any, of extreme obesity. We tested for an interaction between BMI and age and performed sensitivity analyses for death-censored graft failure using BMI as a continuous variable and an integer variable. Statistical analyses were performed using SAS version 9.4 (SAS) and R version 3.4.4 (R Foundation for Statistical Computing).

      Results

      Demographic Features

      The flowchart for inclusion is shown in Fig 1. There were 44,560 recipients available for analysis, among whom 73.9% were alive with a functioning transplant, 6.8% were living after graft failure, 12.6% had died with a functioning kidney transplant, and 6.7% had died following graft failure. Median follow-up was 3.9 (interquartile range, 1.6-6.6) years. BMI was >35 kg/m2 in 5,493 patients (12.3%), >30-35 kg/m2 in 11,144 (25.0%), >25-30 kg/m2 in 15,477 (34.7%), and 18-25 kg/m2 in 12,446 (27.9%). BMI >40 kg/m2 was seen in 847 patients (1.9%). BMI distribution of the 44,560 study participants over time is shown in Fig 2 compared with all adult recipients of first-time, single deceased-donor kidney-alone recipients (N = 127,121) regardless of immunosuppressive regimen or the number of kidneys procured.
      Figure thumbnail gr1
      Figure 1Cohort assembly flow diagram showing inclusion and exclusion criteria for mate-kidney recipient pairs (N = 22,280 pairs).
      Figure thumbnail gr2
      Figure 2BMI distributions of study participants by year (n = 44,560 individuals; black line) and of all first-time adult recipients of single deceased-donor kidneys without other organ transplants from 2001 through 2016 (N = 127,121 individuals; gray line) for comparison.
      Baseline characteristics by BMI group are shown in Table 1. The BMI categorizations of the 22,280 pairs of mate-kidney recipients are shown in Table 2. Compared with patients in the other 3 groups, patients with BMI >35 kg/m2 were more likely to be female and of Black or White race and less likely to be Hispanic or Asian. Patients with BMI >35 kg/m2 also had fewer preemptive transplants and were less likely to have a history of malignancy or seropositivity for CMV, HBV, or HCV, but were more likely to have diabetes and peripheral vascular disease. Recipients with BMI >35 kg/m2 were also more likely to receive kidneys that had been perfused on a pump before transplant and have lymphocyte-depleting induction, but less likely to receive left kidneys or posttransplant glucocorticoids. Among the subset of pairs discordant for BMI ≤35 kg/m2 versus >35 kg/m2 (n = 4,647 pairs), findings were similar except for lower age among the heavier recipients; statistical significance was attenuated for differences in CMV seropositivity, peripheral vascular disease, and glucocorticoid maintenance. The subset of pairs discordant for BMI ≤30 kg/m2 versus >30 kg/m2 (n = 10,087 pairs) retained significant differences in sex, race, HBV and HCV, preemptive transplant, primary diagnosis, diabetes, lymphocyte-depleting induction, and right/left laterality of the allograft (Table 3).
      Table 1Clinical Characteristics of the Study Population of Mate-Kidney Allograft Recipients
      All (N = 44,560)BMI
      18-25 kg/m2 (n = 12,446)>25-30 kg/m2 (n = 15,477)>30-35 kg/m2 (n = 11,144)>35 kg/m2 (n = 5,493)
      Age, y54 ± 1352 ± 1455 ± 1255 ± 1252 ± 12
       Female sex38%40%34%38%45%
      Race
       White39.8%37.4%39.4%42.1%42.0%
       Black34.5%30.8%33.4%36.6%41.8%
       Hispanic16.8%18.0%18.3%15.7%12.1%
       Asian6.6%11.8%6.7%3.2%1.5%
       Other2.3%2.0%2.2%2.4%2.6%
      CMV matching
       Positive to positive44.7%46.2%45.8%43.4%40.8%
       Negative to positive24.7%25.1%24.7%24.5%24.3%
       Positive to negative18.2%16.9%17.6%19.2%20.4%
       Negative to negative12.4%11.8%11.9%12.9%14.5%
      HBV
       Negative78.4%76.6%78.2%79.1%82.0%
       Positive11.3%12.2%10.1%8.7%7.5%
       Unknown11.5%11.2%11.7%12.2%10.5%
      HCV
       Negative92.2%90.9%92.1%93.3%93.4%
       Positive4.7%5.7%4.8%4.0%3.7%
       Unknown3.0%3.4%3.0%2.7%2.9%
      Panel reactive antibody0% [0%-11%]0% [0%-12%]0% [0%-10%]0% [0%-10%]0% [0%-17%]
      Dialysis time, mo49 [29-74]51 [30-79]47 [29-72]48 [30-71]50 [31-73]
      Waitlist time, mo26 [11-44]26 [11-46]30 [11-44]31 [11-44]27 [12-45]
      Preemptive treatment7.2%7.3%7.3%7.3%6.1%
      Primary diagnosis
       Cystic diseases9.0%10.6%9.5%7.7%6.8%
       DM31.2%19.8%31.4%39.2%40.3%
       Glomerulonephritis18.7%23.5%17.7%15.5%16.9%
       Hypertension28.2%29.7%28.8%26.8%26.3%
       Other12.9%16.4%12.6%10.8%9.7%
       DM, not primary kidney diagnosis6.0%3.7%5.9%7.6%8.2%
       Peripheral vascular disease7.1%5.5%7.1%8.5%7.8%
       Prior malignancy6.4%6.1%7.0%6.2%5.5%
       Lymphocyte-depleting induction76.8%75.1%76.1%77.6%81.0%
       Steroid maintenance70.9%72.0%71.0%70.9%68.0%
       Kidney received on pump47.3%44.5%47.2%49.1%49.7%
       Left kidney transplanted50.0%51.9%50.6%48.5%46.8%
      ABO match
       Identical96.2%96.5%96.1%96.2%96.4%
       Compatible3.2%3.0%3.2%3.2%3.2%
       Incompatible0.6%0.5%0.7%0.6%0.4%
       HLA mismatches4 [4-5]4 [4-5]4 [4-5]4 [4-5]4 [4-5]
       DR locus mismatches1 [1-2]1 [1-2]1 [1-2]1 [1-2]1 [1-2]
       Private insurance24.0%23.9%24.4%23.9%23.5%
       Distance to transplant center, mi48 [5-158]41 [6-155]50 [5-163]49 [5-154]50 [5-150]
       Cold ischemia time, h17 [11-23]17 [11-23]17 [12- 23]17 [11-23]17 [11-22]
      Values for continuous variables given as mean ± SD or median [interquartile range]. Abbreviations: ABO, blood type; CMV, cytomegalovirus; DM, diabetes mellitus; DR, class II antigens, tissue typing; HBV, hepatitis B virus; HCV, hepatitis C virus; HLA, human leukocyte antigen, tissue typing.
      Table 2BMI Categorizations of Mate-Kidney Allograft Recipients
      BMI (Larger Recipient)Total
      ≤25 kg/m2>25-≤30 kg/m2>30-≤35 kg/m2>35 kg/m2
      BMI (smaller recipient)
       ≤25 kg/m21,8704,3282,9591,41910,576
       >25-≤30 kg/m202,7203,9561,7538,429
       >30-≤35 kg/m2001,3771,4752,852
       >35 kg/m2000423423
       Total1,8707,0488,2925,07022,280
      Abbreviation: BMI, body mass index.
      Table 3Baseline Characteristics Among Mate-Kidney Recipients Discordant for BMI at Cutoffs of 30 and 35 kg/m2
      BMI Cutoff of 30 kg/m2BMI Cutoff of 30 kg/m2
      ≤30 kg/m2 (n = 10,087)>30 kg/m2 (n = 10,087)P≤35 kg/m2 (n = 4,647)>35 kg/m2 (n = 4,647)P
      Age, y53.7 ± 13.153.9 ± 11.80.353.5 ± 12.852.3 ± 11.5<0.001
      Female sex35.2%40.8%<0.00136.2%45.1%<0.001
      Race<0.001<0.001
       White40.4%41.6%41.7%42.0%
       Black33.1%37.2%36.2%41.6%
       Hispanic16.8%15.7%14.4%12.2%
       Asian7.6%2.9%5.7%1.6%
       Others2.1%2.6%2.0%2.6%
      CMV matching0.50.7
       Positive to positive44.0%43.0%42.5%41.2%
       Negative to positive25.3%24.7%24.9%24.3%
       Positive to negative18.0%19.0%18.7%20.0%
       Negative to negative12.7%13.3%13.9%14.5%
      HBV<0.001<0.001
       Negative78.0%79.6%79.3%81.6%
       Positive10.9%8.6%10.0%7.6%
       Unknown11.1%11.8%10.7%10.8%
      HCV<0.0010.03
       Negative91.9%92.9%92.2%93.2%
       Positive5.3%4.2%4.9%3.8%
       Unknown2.8%2.9%2.9%3.0%
      Panel reactive antibody0% [0%-11%]0% [0%-11%]0.90% [0%-11%]0 [0%-14%]0.5
      Dialysis time, mo49 [29-74]48 [30-72]0.248 [29-73]49 [31-73]0.1
      Waitlist time, mo26 [11-44]26 [11-45]0.426 [11-44]27 [12-45]0.04
      Preemptive transplant7.4%6.7%0.027.8%6.1%0.002
      Primary diagnosis<0.001<0.001
       Cystic diseases10.4%7.4%9.6%6.7%
       DM26.1%39.0%29.3%40.1%
       Glomerulonephritis20.6%16.2%18.7%17.3%
       Hypertension28.7%26.8%29.3%26.2%
       Other14.2%10.6%13.1%9.7%
      DM, not primary kidney diagnosis5.8%8.6%<0.0016.8%9.6%<0.001
      Peripheral vascular disease6.6%8.1%<0.0017.1%7.9%0.1
      Prior malignancy6.5%5.7%0.16.5%5.0%0.01
      Lymphocyte-depleting induction76.3%78.4%<0.00177.8%80.7%<0.001
      Steroid maintenance70.9%70.6%0.569.868.8%0.4
      Kidney received on pump47.9%48.5%0.0549.9%50.0%0.8
      Left kidney transplanted46.6%53.4%<0.00146.3%53.7%<0.001
      ABO match0.80.3
       Identical99.5%99.5%99.5%99.6%
       Compatible0.5%0.5%0.5%0.4%
      HLA mismatches4 [4-5]4 [4-5]0.94 [4-5]4 [4-5]0.1
      DR/locus mismatches1 [1-2]1 [1-2]0.81 [1-2]1 [1-2]0.1
      Private insurance24.2%24.7%0.423.7%23.8%0.9
      Distance to transplant center, mi50 [5-162]50 [5-162]0.450 [6-161]50 [5-154]0.7
      Cold ischemia time, h17 [11-23]17 [12-23]0.517 [12-23]17 [11-22]0.9
      Hospital length of stay, d5 [4-7]5 [4-8]<0.0015 [4-7]5 [4-8]<0.001
      Delayed graft function24.7%31.7%<0.00126.2%34.5%<0.001
      Values for continuous variables given as mean ± SD or median [interquartile range]. Number of paired recipients are given in each BMI category. Abbreviations: ABO, blood type; CMV, cytomegalovirus; DM, diabetes mellitus; DR, class II antigens, tissue typing; HBV, hepatitis B virus; HCV, hepatitis C virus.

      Outcomes

      Outcome measures from unadjusted and adjusted models are shown in Table 4. Compared with patients with a BMI >35 kg/m2, HRs for patient death were 0.93 (95% CI, 0.83-1.04), 0.91 (95% CI, 0.82-1.01), and 0.96 (95% CI, 0.86-1.06) for BMI 18-25, >25-30, and >30-35 kg/m2, respectively; none of these differences met the criteria for statistical significance. Compared with patients with a BMI >35 kg/m2, the HRs for death-censored graft failure were lower in patients with a BMI ≤30 kg/m2 (0.66 [95% CI, 0.59-0.74] and 0.79 [95% CI, 0.70-0.88] for BMI 18-25 and >25-30 kg/m2, respectively). However, the hazard of death-censored graft failure in patients with a BMI >30-35 kg/m2 did not differ from the hazard in patients with a higher BMI (0.91 [95% CI, 0.81-1.02]; P = 0.09). Associations of overall graft failure were similar. However, there were significant, graded associations of lower DGF with decreasing BMI, with 59%, 46%, and 27% lower odds for patients with BMI ≤25, >25-30, and >30-35 kg/m2, respectively (P < 0.001 for all).
      Table 4Unadjusted and Adjusted Transplant Outcomes in Mate-Kidney Models Using BMI >35 kg/m2 as Reference
      BMI 18-25 kg/m2BMI >25-30 kg/m2BMI >30-35 kg/m2P
      P value from testing overall BMI, with 3 degrees of freedom.
      HR or OR
      HR for graft failure and death-censored graft failure; OR for delayed graft function.
      PHR or OR
      HR for graft failure and death-censored graft failure; OR for delayed graft function.
      PHR or OR
      HR for graft failure and death-censored graft failure; OR for delayed graft function.
      P
      Graft failure
       Unadjusted0.81 (0.74-0.89)<0.0010.91 (0.83-0.99)0.0010.98 (0.90-1.08)0.6<0.001
       Adjusted0.83 (0.77-0.90)<0.0010.89 (0.82-0.96)0.0030.96 (0.88-1.03)0.30.002
      Death-censored graft failure
       Unadjusted0.72 (0.64-0.81)<0.0010.78 (0.69-0.87)<0.0010.88 (0.77-1.00)0.01<0.001
       Adjusted0.66 (0.59-0.74)<0.0010.79 (0.70-0.88)<0.0010.91 (0.81-1.02)0.09<0.001
      Death
       Unadjusted0.86 (0.78-0.96)<0.0011.00 (0.91-1.11)0.91.08 (0.97-1.20)0.90.02
       Adjusted0.93 (0.83-1.04)0.20.91 (0.82-1.01)0.080.96 (0.86-1.06)0.40.6
      Delayed graft function
       Unadjusted0.44 (0.37-0.53)<0.0010.58 (0.49-0.68)<0.0010.76 (0.64-0.90)<0.001<0.001
       Adjusted0.41 (0.36-0.48)<0.0010.54 (0.47-0.62)<0.0010.73 (0.64-0.83)<0.001<0.001
      HRs and ORs given with 95% CI in parentheses. Adjusted models were adjusted for age, sex, race, year of transplant, dialysis vintage, time on waitlist, preemptive transplantation, primary kidney failure diagnosis, diabetes, peripheral vascular disease, cytomegalovirus, antibody status for hepatitis B virus and hepatitis C virus, panel-reactive antibody, private insurance, laterality of the allograft, induction type, steroid maintenance, kidney received on pump, distance from donor center, cold ischemia time, ABO blood type match, HLA tissue typing mismatch, and class II antigen tissue typing locus mismatch. Abbreviations: HR, hazard ratio; OR, odds ratio.
      a HR for graft failure and death-censored graft failure; OR for delayed graft function.
      b P value from testing overall BMI, with 3 degrees of freedom.
      Mean LOSs during the transplant admission were 7.2 ± 11.1, 6.9 ± 7.5, 7.1 ± 7.5, and 7.4 ± 14.5 days for BMI <25, >25-30, >30-35, and >35 kg/m2, respectively, with median values of 5 days in all groups. Compared with BMI >35 kg/m2, the adjusted LOS was 0.29 days shorter for those with a BMI >25-30 kg/m2 (P = 0.03), but no other significant differences were observed.

      Sensitivity Analyses

      Among 5,493 patients with a BMI >35 kg/m2, 847 (15%) had BMI >40 kg/m2. These patients, who would be denied access to transplantation in many centers, constituted only 1.9% of the total study population. Results of analyses using BMI >40 kg/m2 as the reference group are shown in Table S1. We found no significant differences in long-term patient or graft outcomes between those in the BMI >40 kg/m2 group and those in the BMI >30-35 and >35-40 kg/m2 groups. Compared with the BMI >40 kg/m2 group, patients in the BMI 18-25 kg/m2 group had lower hazards of adjusted graft failure (0.82 [95% CI, 0.68-0.99]; P = 0.004) and death-censored graft failure (0.76 [95% CI, 0.59-0.97]; P = 0.002). Adjusted HRs for patient death did not differ significantly across BMI groups. Intermediate outcomes of acute rejection and graft failure during the first year after transplant are shown in Tables S2 and S3. Interactions between BMI and age were not seen in any of the models. Analyzed as a continuous variable, BMI had an overall HR for death-censored graft failure of 1.015 (95% CI, 1.010-1.020) per unit increase in BMI (P = 0.001). Analyzed as an integer variable, HRs for death-censored graft failure and patient death increased gradually with increasing BMI, without any obvious break point at which risk accelerated (Figs S1-S2).

      Discussion

      Using a mate-kidney model, we showed that patients with a BMI >35 kg/m2 had uniformly poorer graft outcomes than patients without obesity (BMI ≤30 kg/m2), but did not demonstrate any significant differences in long-term outcomes between patients with a BMI >35 kg/m2 and those with a BMI of 30-35 kg/m2. Obesity was associated with modest gradual increases in overall and death-censored graft failure, without an obvious threshold value. This latter fact is important, as current practice is that patients with a BMI of 30-35 kg/m2 routinely receive kidney transplants, whereas those with a BMI >35 kg/m2 often do not. We did not observe increased patient mortality among recipients with a BMI >35 kg/m2 compared with any other groups. Delayed graft function was associated with increasing BMI, consistent with prior work, but we did not observe any differences in the LOS of the transplant admission, suggesting that this complication may have often been amenable to outpatient management. Our results were equally applicable to the subgroup of patients with a BMI >40 kg/m2. Taken together with the survival advantage of transplantation over dialysis in recipients with obesity, our findings suggest that strict BMI thresholds for kidney transplantation may deserve reconsideration.
      Prior observational studies have yielded conflicting results regarding the effects of recipient obesity on kidney transplant outcomes. Obesity has been associated with increased DGF in many analyses and also with variable increases in surgical wound complications, increased hospital LOS, acute rejection, increased serum creatinine, proteinuria, and new-onset diabetes mellitus after transplantation (NODAT).
      • Kwan J.M.
      • Hajjiri Z.
      • Metwally A.
      • Finn P.W.
      • Perkins D.L.
      Effect of the obesity epidemic on kidney transplantation: Obesity is independent of diabetes as a risk factor for adverse renal transplant outcomes.
      ,
      • Cannon R.M.
      • Jones C.M.
      • Hughes M.G.
      • Eng M.
      • Marvin M.R.
      The impact of recipient obesity on outcomes after renal transplantation.
      ,
      • Aalten J.
      • Christiaans M.H.
      • de Fijter H.
      • et al.
      The influence of obesity on short and long-term graft and patient survival after renal transplantation.
      ,
      • Gore J.L.
      • Pham P.T.
      • Danovitch G.M.
      • et al.
      Obesity and outcome following renal transplantation.
      ,
      • Hoogeveen E.K.1
      • Aalten J.
      • Rothman K.J.
      • et al.
      Effect of obesity on the outcome of kidney transplantation; a 20-year follow-up.
      • Chang S.H.
      • Coates P.T.
      • McDonald S.P.
      Effects of body mass index at transplant on outcomes of kidney transplantation.
      • Ditonno P.
      • Lucarelli G.
      • Impedovo S.V.
      • et al.
      Obesity in kidney transplantation affects renal function but not graft and patient survival.
      • Furriel F.
      • Parada B.
      • Campos L.
      • et al.
      Pretransplantation overweight and obesity: does it really affect kidney transplantation outcomes?.
      • Holley J.L.
      • Shapiro R.
      • Lopatin W.B.
      • Tzakis A.G.
      • Hakala T.R.
      • Starzl T.E.
      Obesity as a risk factor following cadaveric renal transplantation.
      • Molnar M.Z.
      • Kovesdy C.P.
      • Mucsi I.
      • et al.
      Higher recipient body mass index is associated with post-transplant delayed kidney graft function.
      • Weissenbacher A.
      • Jara M.
      • Ulmer H.
      • et al.
      Recipient and donor body mass index as important risk factors for delayed kidney graft function.
      A meta-analysis of 17 studies (138,081 patients) demonstrated increased DGF and death-censored graft loss in recipients with obesity, without significant difference in mortality risk.
      • Hill C.J.
      • Courtney A.E.
      • Cardwell C.R.
      • et al.
      Recipient obesity and outcomes after kidney transplantation; a systematic review and meta-analysis.
      Another meta-analysis of 21 studies (241,381 patients) reported increased mortality, biopsy-proven acute rejections, graft failure, and DGF among obese kidney recipients.
      • Sood A.
      • Hakim D.N.
      • Hakim N.S.
      Consequences of recipient obesity on postoperative outcomes in a renal transplant: a systematic review and meta-analysis.
      In a third meta-analysis, obesity was associated with DGF but not acute rejection; increased graft loss and mortality were associated with obesity only within studies of patients undergoing transplantation before 2000.
      • Nicoletto B.B.
      • Fonseca N.K.
      • Manfro R.C.
      • Goncalves L.F.
      • Leitao C.B.
      • Souza G.C.
      Effects of obesity on kidney transplantation outcomes: a systemic review and meta-analysis.
      In a fourth meta-analysis, patients with a BMI <30 kg/m2 had lower incidences of DGF, acute rejection, wound complications, incisional hernia, NODAT, and hypertension, as well as shorter surgery duration and decreased LOS, with improved graft but similar patient survival.
      • Lafranca J.A.
      • IJzermans J.N.
      • Betjes M.G.
      • Dor F.J.
      Body mass index and outcome in renal transplant recipients: a systematic review and meta-analysis.
      Our analysis used a mate-kidney model to minimize biases related to donor organ quality. Recipient habitus could exert conscious or unconscious influence on the decision to accept or reject an allograft offer. For example, a surgeon might hesitate to implant a kidney with a high Kidney Donor Profile Index or a kidney donated after cardiac death into a patient with obesity because of concerns for DGF and poor wound healing. In addition, kidneys from pediatric donors and small female donors might be avoided because of concern that donor/recipient body size mismatch might result in inadequate nephron mass for a recipient with obesity. Our data suggest that left kidneys, which have longer blood vessels, may be preferentially implanted in recipients with obesity. Mate-kidney analysis is a valid method to control for donor variables and organ quality that has been used previously to compare perioperative induction regimens and evaluate the impact of cold ischemia time on the outcomes following transplantation of marginal kidneys.
      • Sampaio M.S.
      • Chopra B.
      • Sureshkumar K.K.
      Depleting antibody induction and kidney transplant outcomes: a paired kidney analysis.
      ,
      • Sampaio M.S.
      • Chopra B.
      • Tang A.
      • Sureshkumar K.K.
      Impact of cold ischemia time on the outcomes of kidneys with KDPI >85%: mate kidney analysis.
      Two small single-center studies have used mate-kidney models to evaluate the impact of obesity on kidney transplant outcomes.
      • Yamamoto S.
      • Hanley E.
      • Hahn A.B.
      • et al.
      The impact of obesity in renal transplantation: an analysis of paired cadaver kidneys.
      ,
      • Wolyniec Z.
      • Debska-Slizien A.
      • Woliniec W.
      • Rutkowski B.
      Impact of obesity on renal graft function-analysis of kidney grafts from the same donor.
      Yamamoto et al compared the outcomes of kidney transplants from the same deceased donor (N = 28) into recipients with a BMI >30 kg/m2 versus <30 kg/m2 and found similar rates of DGF and acute rejection. Patient survival was similar, but 5-year graft survival was inferior in the high-BMI recipients.
      • Yamamoto S.
      • Hanley E.
      • Hahn A.B.
      • et al.
      The impact of obesity in renal transplantation: an analysis of paired cadaver kidneys.
      Wolyniec et al more recently compared short-term outcomes of mate-kidney transplants from deceased donors (N = 37) in one recipient with a BMI >30 kg/m2 and the other with a BMI <30 kg/m2.
      • Wolyniec Z.
      • Debska-Slizien A.
      • Woliniec W.
      • Rutkowski B.
      Impact of obesity on renal graft function-analysis of kidney grafts from the same donor.
      Incidence of DGF, acute rejection, and 1-year graft and patient survival rates were similar, and estimated glomerular filtration rate did not differ between groups during the first year after transplantation. However, patients with obesity experienced more wound complications, had longer hospital LOS, and experienced NODAT at a higher rate.
      • Wolyniec Z.
      • Debska-Slizien A.
      • Woliniec W.
      • Rutkowski B.
      Impact of obesity on renal graft function-analysis of kidney grafts from the same donor.
      Consistent with previous studies, we observed a progressive increase in the risk of DGF with increasing BMI that was robust to adjustment for many variables. Potential explanations include prolonged surgical time often needed to perform kidney transplants in patients with obesity, along with vasoconstriction from obesity-related increased sympathetic activity, which can promote ischemic allograft injury.
      • Molnar M.Z.
      • Kovesdy C.P.
      • Mucsi I.
      • et al.
      Higher recipient body mass index is associated with post-transplant delayed kidney graft function.
      Immunologic and toxic injuries, along with hemodynamic instability, might be more likely among obese recipients as a result of less predictable volumes of distribution for medications. DGF may have contributed to increased death-censored graft failure among the high-BMI groups in this analysis, but there were several other plausible explanations. Obesity can result in hemodynamically mediated hyperfiltration, proteinuria, and glomerulopathy, with resultant focal segmental glomerulosclerosis.
      • Kambham N.
      • Markowitz G.S.
      • Valeri A.M.
      • Lin J.
      • D’Agati V.D.
      Obesity related glomerulopathy. An emerging epidemic.
      Obesity is associated with excess inflammation and altered immune responses, which may help explain why biopsy-proven acute rejection is observed more frequently in kidney transplant recipients with obesity.
      • Sood A.
      • Hakim D.N.
      • Hakim N.S.
      Consequences of recipient obesity on postoperative outcomes in a renal transplant: a systematic review and meta-analysis.
      Obese transplant recipients might have lower effective concentrations of induction agents and maintenance therapies such as calcineurin inhibitors, allowing suboptimal immunosuppression that may enable immune-mediated allograft injury and loss.
      • Halloran P.F.
      Immunosuppressive drugs for kidney transplantation.
      Proinflammatory cytokines produced by adipose tissue could also promote kidney injury.
      Adjusted mortality was similar across all BMI groups. This observation may appear counterintuitive because obesity is associated with an increased mortality risk in the general population that is primarily driven by cardiovascular disease. Selection bias among the transplant recipients with obesity could in part explain this observation, as patients undergo rigorous assessments before being placed on transplant waiting lists. Those with significant cardiovascular conditions may not be listed, resulting in a group of obese recipients who are more highly selected and healthier than obese patients with advanced chronic kidney disease in general. Obese patients have been consistently noted to have better survival with dialysis than their nonobese counterparts.
      • Doshi M.
      • Streja E.
      • Rhee C.M.
      • et al.
      Examining the robustness of the obesity paradox in maintenance hemodialysis patients: a marginal structural model analysis.
      This “obesity paradox” may confer an advantage upon obese patients after transplantation, as well as during their time on the waiting list.
      There are currently no evidence-based recommendations for a BMI limit for kidney transplantation. Transplant centers vary in their policies regarding a maximum acceptable BMI for candidate waitlisting and kidney transplantation. Most centers begin to exclude candidates when BMI is >30 kg/m2. According to the European Best Practice Guidelines, potential transplant candidates with a BMI >30 kg/m2 should lose weight before being placed on the waiting list.
      European Renal Best Practice Transplantation Guideline Development Group
      ERBP guideline on the management and evaluation of the kidney donor and recipient.
      We observed no differences in risks of graft failure or patient death between groups with BMI >30-35 kg/m2 and >35 kg/m2 in our mate-kidney analysis. This is important because patients with a BMI >35 kg/m2 have a survival benefit with kidney transplantation compared with maintenance dialysis that is similar to that in patients with normal BMI.
      • Gill J.S.
      • Lan J.
      • Dong J.
      • et al.
      The survival benefit of kidney transplantation in obese patients.
      Moreover, observational studies have not consistently demonstrated patient and graft survival benefits associated with pretransplant weight loss.
      • Lentine K.L.
      • Delos Santos R.
      • Axelrod D.
      • Schnitzler M.A.
      • Brennan D.C.
      • Tuttle-Newhall J.E.
      Obesity and kidney transplant candidates: how big is too big for transplantation?.
      Advances in transplantation surgery, with increased use of laparoscopic and robotic techniques, may reduce the operative risks of surgery in the more obese recipient. Our findings provide support for favorable consideration of patients with obesity seeking kidney transplantation, provided that such cases are diligently optimized before transplant.
      The strengths of this study include the use of a large, national database and use of a mate-kidney model for the analysis. Certain limitations nonetheless merit discussion. Our study is retrospective and observational, providing associations rather than causation. Moreover, the mate-kidney model increased the number of exclusions, as exclusion of either recipient resulted in the loss of a pair. This could potentially limit the generalizability of our results. Despite the use of a paired multivariable model, unidentified confounders may have potentially influenced our results. Doses of immunosuppressive medications and drug levels were not available, nor were details of anatomic variability of transplanted organs or data regarding surgical damage at the time of organ procurement, all of which could potentially impact graft outcomes. Laterality of the donor kidney could be a potential confounder of graft outcomes, as some reports suggest a potential benefit from receiving a left kidney, which often comes with a longer renal vein.
      • Vacher-Coponat H.
      • McDonald S.
      • Clayton P.
      • Loundou A.
      • Allen R.D.
      • Chadban S.J.
      Inferior early posttransplant outcomes for recipients of right versus left deceased donor kidneys: an ANZDATA analysis.
      ,
      • Phelan P.J.
      • Shields W.
      • O’Kelly P.
      • et al.
      Left versus right deceased donor renal allograft outcome.
      We had no data regarding postoperative wound infections and complications. Our study examined only patients who were listed and eventually received a transplant, introducing a selection bias toward a healthier cohort in all BMI groups. We used BMI as the sole marker of obesity in our analysis. High BMI may be associated with good muscle mass and a good prognosis in some patients, whereas normal BMI may hide sarcopenic obesity in others. Other measures of obesity such as waist circumference, hip-to-waist ratio, and fat distribution might be more predictive of outcomes. In one study, waist circumference was found to be a better prognostic marker than BMI for obesity, and greater waist circumference was strongly associated with kidney recipient mortality after adjustment for BMI.
      • Kovesdy C.P.
      • Czira M.E.
      • Rudas A.
      • et al.
      Body mass index, waist circumference and mortality in kidney transplant recipients.
      Exact timing of updated BMI measurements were not available in the data, raising the possibility that some measurements were in the distant past. However, the 98% correlation between BMI at listing and at transplant was reassuring. Further study may be required to identify the factors that predict transplant outcomes in recipients with obesity.
      Our findings suggest that patients with extreme obesity and kidney disease have acceptable kidney transplant outcomes even when adjusting for donor characteristics and need not be systematically denied the benefits of kidney transplantation if they are otherwise suitable candidates.

      Article Information

      Authors’ Full Names and Academic Degrees

      Kalathil K. Sureshkumar, MD, Bhavna Chopra, MD, Michelle A. Josephson, MD, Pratik B. Shah, MD, and Rita L. McGill, MD.

      Authors’ Contributions

      Conception and design: KKS, BC, RLM, MAJ, PBS; analysis and interpretation of the data: KKS, BC, RLM, MAJ, PBS; statistical analysis: RLM. Each author contributed important intellectual content during manuscript drafting or revision and agrees to be personally accountable for the individual’s own contributions and to ensure that questions pertaining to the accuracy or integrity of any portion of the work, even one in which the author was not directly involved, are appropriately investigated and resolved, including with documentation in the literature if appropriate.

      Support

      None.

      Financial Disclosure

      The authors declare that they have no relevant financial interests.

      Prior Presentation

      Presented in part at ASN Kidney Week October 2018, San Diego, CA.

      Disclaimer

      The data reported here have been supplied by the United Network for Organ Sharing as the contractor for the Organ Procurement and Transplant Network. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy of or interpretation by OPTN or the US Government.

      Peer Review

      Received May 22, 2020. Evaluated by 2 external peer reviewers, with direct editorial input from a Statistics/Methods Editor, an Associate Editor, and the Editor-in-Chief. Accepted in revised form February 4, 2021.

      Supplementary Material

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