Donor Factors Influencing Graft Outcomes in Live Donor Kidney Transplantation : Transplantation

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Original Articles: Clinical Transplantation

Donor Factors Influencing Graft Outcomes in Live Donor Kidney Transplantation

Issa, Naim1; Stephany, Brian1; Fatica, Richard1; Nurko, Saul1; Krishnamurthi, Venkatesh2; Goldfarb, David A.2; Braun, William E.1; Dennis, Vincent W.1; Heeger, Peter S.1,2,3; Poggio, Emilio D.1,4

Author Information
Transplantation 83(5):p 593-599, March 15, 2007. | DOI: 10.1097/
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Because of the increasing shortage of deceased donors, living donor kidney transplantation has become an attractive alternative for patients with end-stage renal disease. Favorable graft outcomes and the elective nature of the procedure have incited patients and transplant centers to prefer this means of organ transplantation (1). Nonetheless, living donor organs are not identical, and the heterogeneity of these allografts could contribute to the known variability of outcomes in recipients of living renal transplants. Various donor characteristics are routinely used to assess deceased donor organ quality (i.e., age, gender, medical history, ischemia time, etc.) aiding the clinician in risk-stratification of patients for poor outcomes (2 3). Certain live donor characteristics may also help to predict intermediate/long-term graft outcomes. Understanding the donor factors that can influence live donor graft outcomes may prove clinically useful for selecting among multiple potential donors. Furthermore, to minimize quality differences of the exchanged organs, the increasing number of paired kidney exchange programs require a comprehensive assessment of donor factors and their cumulative effects that could clinically influence the outcome of living transplantation (4–6).

Among living donor factors reported to correlate with posttransplant outcome are pretransplant glomerular filtration rate (GFR) (7, 8) and donor age (7, 9, 10). Some authors suggested that donors with higher than “normal” blood pressure may also be considered acceptable candidates for donation (11, 12). Previous reports have involved limited numbers of patients and have not included analyses of the confounding effects of aging, decline in GFR, and increase in blood pressure in the normal subjects (13). Other donor characteristics such as metabolic abnormalities like hyperlipidemia may also be markers of overall organ quality warranting scientific exploration. Hyperlipidemia is a common problem in western societies (14), contributing to an increased risk for cardiovascular and chronic kidney disease mainly due to microvascular pathologic condition and inflammation (15). Scarce evidence exists studying the influence of donor serum lipid levels on long-term graft outcomes. We therefore hypothesized that a careful analysis of donor demographic and laboratory characteristics would reveal independent associations among different live donor characteristics and their cumulative effects on posttransplant graft function and outcomes.


This study was approved by the Institutional Review Board at the Cleveland Clinic. A retrospective chart review of all 264 live donor–recipient pairs transplanted between April 1997 and December 2003 at our institution was performed. Sixteen donor–recipient pairs were excluded from the analysis due to: recipient younger than 18 years old (n=5), primary nonfunction because of postoperative graft artery thrombosis (n=1), graft loss because of irreversible acute rejection (AR) within 1 month posttransplant (n=1), early recurrent glomerular disease (n=1), death with functional graft (n=3), and lost to follow up (n=5). Of the remaining 248 study subjects, the original disease for the study population was as follows: diabetes mellitus (type I or type II) (n=51, 20.5%), hypertension (n=21, 8.5%), glomerulonephritis (n=88, 35.6%), polycystic kidney disease (n=34, 13.7%), urological pathologic condition (n=46, 18.5%), and other unknown etiology (n=8, 3.2%).

We collected donor demographics (age, gender, race, body mass index [BMI], and body surface area [BSA]) and fasting laboratory variables (glucose, GFR, serum creatinine [SCr], uric acid and lipid levels including total serum cholesterol, serum low density lipoprotein [LDL], high density lipoprotein [HDL], and serum triglycerides) on all subjects. Two systolic (SBP) and diastolic (DBP) blood pressure measurements were taken by hypertension-trained nurses in the sitting position 5 minutes apart. An average of the two readings was used for analyses. Absence of proteinuria in timed urine collections is a requirement in our program for kidney donation. Pretransplant donor GFR was measured at the Renal Function Laboratory at the Cleveland Clinic by using 125I-iothalamate urinary clearances (iGFR) as previously described (16). We used the iGFR uncorrected for the donor BSA because it is the absolute donor GFR irrespective of the donor size which determines the “dose” of renal function donated to the recipient (8, 17). We also collected recipient demographic, laboratory, and clinical data including living biologically related or unrelated donation, occurrence of delayed graft function (DGF) or AR. DGF was defined as the requirement of dialysis within 1 week after transplantation. AR was recorded as present for pathologic grades greater than “borderline” by the Banff'97 classification (18). No protocol biopsies were performed, thus all biopsies were performed at the discretion of the treating physician based on clinical suspicion for AR. We defined allograft outcomes based on (1) estimated GFR (eGFR) at 2 years posttransplant using the abbreviated Modification of Diet in Renal Disease (MDRD) study equation (19) (no iGFR measurements were available for the posttransplant period), (2) the occurrence of biopsy-proven AR, and (3) the composite of AR, DGF, or graft loss by 2 years posttransplantation. The immunosuppressive regimen at 2 years posttransplant, chosen at the discretion of the attending physician, consisted of a calcineurin inhibitor (CNI)-based regimen using either cyclosporine or tacrolimus (n=132) or a CNI-free regimen using sirolimus (SRL) (n=116). All patients were also treated with an interleukin (IL)-2 receptor blocker or T cell-depleting antibodies and received mycophenolic mofetil and steroids.

To facilitate the analysis and interpretation of the results, the donor data were subdivided into subgroups according to donor age less than or more than 45 years old, donor iGFR of less than or more than 110 mL/min, SBP less than or more than 120 mm Hg and donor total cholesterol levels less than or more than 200 mg/dL. The use of donor LDL levels more than 130 mg/dL yielded similar results to those obtained by using total donor cholesterol, thus, the analyses of this study focus on the latter. These subdivisions were based on thresholds associated with poor graft outcomes for donor age and iGFR (which were close to the median values of each variable), and on the desirable total serum cholesterol and LDL levels according to the National Cholesterol Education Program guidelines (20). We used the cutoff values recommended by the seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC 7) for normal/optimal SBP (<120 mm Hg) to divide our groups, however, higher cutoff values yielded similar results. We opted to dichotomize the data for clarity of result interpretation. Nevertheless, analyzing these variables in a continuous fashion did not change the results and the interpretation of the data.

Recognizing the lack of availability of isotope clearances for the evaluation of donor GFR by most institutions, we also calculated the donor GFR by using the 4-variable MDRD equation (21) to test if similar statistical relationships were observed by this means when compared with the gold standard isotope clearance. However, because of the complexity and yet unanswered issues relating to the use of this method in potential kidney donors, we mainly focused on the GFR as measured by the radioisotope clearance (22).

Statistical Analysis

Statistical analyses were performed using SPSS software version 11.5 (Chicago, IL). Data are expressed as mean±SD (minimum–maximum) or as n (%) for continuous and categorical variables, respectively, unless otherwise specified. Univariable analysis was performed using chi-square/Fisher exact test and Student's t test methods for categorical/ordinal and continuous data, respectively. All data were normally distributed with the exception of donor iGFR which showed a slight shift to the right. Natural logarithmic transformation of this variable yielded similar results. Nevertheless, the variable was handled as a dichotomous one. ANOVA was used when appropriate. Multivariable linear regression models were constructed to study independent associations of donor and recipient variables with eGFR at 2 years. A P value of <0.05 was considered statistically significant.


Donor and Recipient Characteristics

Baseline donor and recipient characteristics are presented in Table 1. Donor age was 42±10 years, 60% were females, and 84% were white. The median donor absolute iGFR was 111 mL/min (range, 74–191 mL/min), the mean SBP was 117±16 mm Hg, and the mean total serum cholesterol level was 201±40 mg/dL. Recipient age was 44±13 years, 41% were females, and 81% were white. Sixty-seven percent of the recipients received a kidney from a biologically-related donor. CNI-based and SRL-based immunosuppressive regimens were similarly distributed among transplant recipients. AR was detected in 12% of the recipients, and DGF occurred in 6% of the cases. Fourteen recipients (5.6%) lost their graft by 2 years posttransplant. The causes of graft loss were: chronic allograft nephropathy (n=8), noncompliance (n=2), intractable AR (n=3), and other (n=1).

Overall donor and recipient characteristics

Correlation Between Donor iGFR, Age, Blood Pressure, and Lipid Levels With Posttransplant Graft Function by Univariable Analyses

Recipients of kidneys from donors younger than 45 years old when compared with older donors showed significantly better eGFR at 2 years (60±20 vs. 52±18 ml/min/1.73 m2, P<0.01) (Fig. 1A). Furthermore, recipients of organs from donors with an iGFR more than 110 mL/min showed significantly higher eGFR at 2 years posttransplantation when compared with those recipients of organs from donors with lower iGFR (60±21 vs. 53±16 mL/min/1.73 m2, P<0.01) (Fig. 1B). Recipients of donors with an office SBP less than 120 mm Hg also demonstrated significantly higher eGFR at 2 years than recipients of donors with higher blood pressure (59±19 vs. 54±19 ml/min/1.73 m2, P=0.02) (Fig. 1C). Interestingly, as shown in Figure 1D donor lipids levels were related to graft function. Recipients of grafts from donors with lower total cholesterol levels showed better 2-year graft function (62±20 mL/min/1.73 m2 for donors with total cholesterol levels less than 200 mg/dL vs. 52±18 mL/min/1.73 m2 for those with higher total cholesterol levels, P<0.01). Similar findings were observed for LDL levels less than 130 mg/dL versus those recipients of organs from donors with higher LDL levels (58±20 mL/min/1.73 m2 vs. 51±19 mL/min/1.73 m2, P=0.02). No relationship between graft function and donor triglyceride, HDL, uric acid, fasting glucose, gender, or race was detected. All reported differences were also present at 3 years in those patients who reach such posttransplant follow up (Fig. 1).

Association between recipient eGFR at 1, 2, and 3 years and donor age (A), donor iGFR (B), donor systolic blood pressure (C), and total cholesterol levels (D). N.B. The effects of donor iGFR on graft estimated GFR were more pronounced for patients treated with a CNI inhibitor (data not shown).

Cumulative Effects of Donor Factors on Graft Function

We next asked whether donor age, GFR, blood pressure, and total cholesterol levels were cumulatively related to eGFR at 2 years posttransplantation irrespective of the combination of factors. That is, all factors were statistically weighted equally and donors were subdivided on whether they had no factors (i.e., young donors, with iGFR ≥110 mL/min, SBP ≤120 mm Hg, and total cholesterol <200 mg/dL, categorized as “none” in Fig. 2) or they shared factors (i.e., older donors, with iGFR <110 mL/min, SBP >120 mm Hg and total cholesterol >200 mg/dL, categorized as “4 factors”, with other combinations in between). As shown in Figure 2, lower estimated graft function at 2 years was directly correlated with the number of individual donor risk factors (P<0.001 by ANOVA).

Error bars showing mean (and 95% confidence intervals) estimated graft GFR (mL/min per 1.73 m2) based on the number of tested donor factors (age ≥45 years, iGFR <110 mL/min, total cholesterol ≥200 mg/dL and/or systolic blood pressure ≥120 mm Hg).

Donor Age, Cholesterol, GFR, and Systolic Blood Pressure as Independent Correlates of Poor Outcomes

To determine which factors independently correlate with graft function at 2 years posttransplantation, a multivariable linear regression model was constructed using donor and recipient characteristics including immunosuppressive regimen, and history of AR or DGF (Table 2). All donor variables that relate to size of the donor–organ (male gender, BSA, unadjusted GFR, and eGFR by MDRD equation) are associated with graft outcomes as previously reported (17). Therefore, because of co-linearity among these factors, only unadjusted donor GFR was used as a marker of size in the multivariable analyses. Donor age, donor iGFR, donor SBP, and donor cholesterol level correlated with graft function at 2 years by univariable analyses (Table 2). A history of AR was also associated with poorer graft eGFR at 2 years (slope −8.23 mL/min/1.73 m2, P=0.04) with a trend toward lower eGFR in recipients with a history of DGF or CNI use (slopes of −5.4, P=0.30 and −2.19, P=0.38, respectively). In a multivariable linear regression model older donors (−5.53, P=0.03), iGFR less than 110 mL/min (slope −6.13 mL/min/1.73 m2 of graft eGFR, P<0.01) and total cholesterol level more than 200 mg/dL (slope −7.43 mL/min/1.73 m2 of graft eGFR, P<0.01) were independently associated with inferior graft function at 2 years posttransplantation. Donor SBP more than 120 mm Hg also correlated with poorer graft function (slope −5.60 mL/min/1.73 m2, P=0.04). Similarly, a history of AR remained independently associated with worse graft function (slope −8.23 mL/min/1.73 m2, P=0.04).

Multivariable linear regression analysis showing donor independent variables associated with estimated GFR at 2 yr (mL/min/1.73 m2) posttransplantation

As shown in Figure 3, higher donor GFR along with lower cholesterol levels were additive as correlates of graft function at 2 years posttransplantation. Recipients of grafts from donors with an iGFR more than 110 mL/min and serum total cholesterol levels less than 200 mg/dL had a mean eGFR of 69±18 mL/min/1.73 m2, whereas those recipients of allografts from donors with an iGFR less than 110 mL/min and total cholesterol level of more than 200 mg/dL had an eGFR of 50±15 mL/min/1.73 m2 (P<0.01). Furthermore, as shown in Figure 4A, the incidence of AR was diagnosed less often in recipients who received grafts from donors with iGFR more than 110 mL/min and total cholesterol levels less than 200 mg/dL (3/59 [5.0%] vs. 16/64 [25%], P=0.04 by Fisher's exact test). A composite endpoint of either AR, DGF, and/or graft loss by 2 years posttransplantation was found in only 4/59 (6.8%) recipients of organs from donors with iGFR more than 110 mL/min and total cholesterol levels less than 200 mg/dL versus 18/64 (28.0%) recipients of organs from donors with lower GFR and elevated lipids (P<0.01) (Fig. 4B).

Relationship between eGFR at 2 years with donor iGFR and total serum cholesterol levels (P<0.01).
Relationship between the incidence of AR (in black) (Fig. 4A) and a composite of AR, DGF, and/or graft failure (in black) (Fig. 4B) by 2 years posttransplantation with donor iGFR and serum cholesterol levels.


The main finding of our study is that living donor characteristics, specifically donor age, cholesterol levels, GFR, and systolic blood pressure, are important and independent correlates of recipient allograft function at two years posttransplantation, confirming that not all living donors provide equal quality of organs. Moreover, the presence of multiple individual factors associated with poor outcome seemed to have a cumulative effect. Routine assessment of these donor characteristics before living donor transplantation may allow for more accurate prediction of allograft success, and risk stratification of organs from prospective kidney donors.

The results of our study add to the current medical knowledge in several ways. First, we found the somewhat surprising finding that elevated donor cholesterol levels were independently associated with lower graft function. This correlation was present and strong even after adjusting for covariates known to be associated with the aging population (lower GFR and higher blood pressure), thus minimizing the potential confounding effects of these factors on the observed association. A question that immediately comes to mind is how one can relate high donor cholesterol levels to the fate of those kidneys once transplanted? The mechanisms to account for this relationship remain speculative. Studies in animals have demonstrated that hyperlipidemia contributes to the development and progression of renal disease irrespective of other factors (23, 24). Furthermore, acute immune-mediated injury and hypercholesterolemia are synergistic in causing chronic vascular immune-mediated injury (25). Hyperlipidemia is a common problem in humans (14) and associations between dylipidemias and decreased renal function have been reported (26–29). Epidemiological studies using data derived from the Third National Health and Nutrition Examination Survey provide indirect evidence on the association of cardiovascular risk factors, including hypercholesterolemia, and low levels of GFR (27). Although the pathogenesis of these injuries remain poorly understood, we speculate that hyperlipidemia induces subclinical renal microvascular or glomerular injury in the donor that could predispose the graft to a lower threshold for immune and non-immune mediated injury in the posttransplant period. Based on the recently demonstrated reduction in lipid related vascular injury by the use of a statin (30), future studies in assessing effects of donor and recipient statin therapy on long-term graft function will provide important information.

Our study also confirms and extends previous work by demonstrating a strong independent association between donor GFR and graft function at 2 years (7, 8, 10, 17). The independent correlation between donor GFR and graft function after adjusting for donor age (among others) which is known to influence GFR highlights the novelty of our results (13). One explanation for this association is the “nephron mass” hypothesis—that a larger “nephron reserve” allows the transplant recipient to maintain lower serum creatinine levels despite multiple graft injuries (i.e., CNI toxicity, DGF, AR, etc.) (17, 31). Our data further suggest that organs derived from living donors with high GFR and low total cholesterol levels result in better graft function, less AR, DGF, and graft loss than those derived from hyperlipidemic donors with lower GFR. A potential explanation for this observation might be that lower donor GFR may provide a lower renal functional reserve, and thus clinical rejection as manifested by a rise in creatinine is more likely to prompt a diagnostic biopsy in this subgroup of patients. Protocol biopsies would be required to clarify this issue. Another important observation is the noted independent correlation between normotensive donors and graft function. Textor et al. reported nonstatistically significant lower measured GFR levels at 1 year posttransplantation in a smaller cohort of recipients of hypertensive living donors (BP >140/90 mm Hg) (11). We used the definition of “normal” blood pressure (as defined by the JNC 7 report) (32) to show that SBP may also be an important determinant of graft function in living donation at 2 years posttransplantation. Other tested metabolic parameters (fasting glucose and uric acid concentrations) were not found to correlate with graft outcomes.

Another key finding derived from this study is the additive negative effects of increasing number of donor factors on graft function (Fig. 2), a result that is confirmed in larger studies can aid the clinician in assessing the “organ quality” of the potential donor. These findings raise several important questions: Who is a “healthy” donor? Is a person with “minimal” burden of disease suitable for donation? What would be the long-term consequences of dyslipidemia-abnormal blood pressure on the remaining kidney in the donor if left untreated? The long-term studies on the safety of living organ donation were performed in an era in which the acceptability criteria for organ donation was more strict and rigorous (33, 34), thus future studies looking at the long-term outcomes of donors from recent years are also warranted.

We acknowledge several limitations. This is a retrospective single center study with a relatively short follow up. Both factors limit our ability to state with certainty that donor iGFR, lipid levels, and blood pressure predict long-term graft survival in living renal transplantation beyond this time period and outside this cohort of patients; however, based on the available 3-year data these findings seem to persist. Previous reports correlating graft function with long-term graft survival (35) imply that our results are likely to hold up over longer follow up. Donor evaluation at our institution includes only timed urine collection to detect proteinuria, and we do not routinely check for microalbuminuria which may be an early manifestation of kidneys with subclinical injury. We also recognize that calculated GFR by estimation equations to assess graft function has limitations. However, the MDRD study equation that we used herein has performed better than other formulas in a U.S. renal transplant population (36).

In conclusion, analogous to deceased organ donation, donor characteristics can influence the quality of the donated organ in live donor kidney transplantation. Although we do not advocate eliminating a potential donor based on the information derived from this work, assessment of these donor parameters before live donor transplantation may permit more accurate prediction of allograft success by risk-stratifying donor– recipient pairs. Older donors with added comorbidities warrant closer follow up postdonation.


The authors thank Mrs. Sandra Bronoff for her invaluable editorial assistance.


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Glomerular filtration rate; Cholesterol; Living donor; Kidney transplantation; Graft outcome; Age; Blood pressure

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