Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

What is the impact of human leukocyte antigen mismatching on graft survival and mortality in renal transplantation? A meta-analysis of 23 cohort studies involving 486,608 recipients

  • 1,
  • 2, 3, 4, 5,
  • 6, 7,
  • 1,
  • 2, 3, 4, 5,
  • 1 and
  • 1Email author
BMC Nephrology201819:116

https://doi.org/10.1186/s12882-018-0908-3

  • Received: 1 October 2017
  • Accepted: 26 April 2018
  • Published:
Open Peer Review reports

Abstracts

Background

The magnitude effects of human leukocyte antigen (HLA) mismatching on post-transplant outcomes of kidney transplantation remain controversial. We aim to quantitatively assess the associations of HLA mismatching with graft survival and mortality in adult kidney transplantation.

Methods

We searched PubMed, EMBASE and the Cochrane Library from their inception to December, 2016. Priori clinical outcomes were overall graft failure, death-censored graft failure and all-cause mortality.

Results

A total of 23 cohort studies covering 486,608 recipients were selected. HLA per mismatch was significant associated with increased risks of overall graft failure (hazard ratio (HR), 1.06; 95% confidence interval (CI), 1.05–1.07), death-censored graft failure (HR: 1.09; 95% CI 1.06–1.12) and all-cause mortality (HR: 1.04; 95% CI: 1.02–1.07). Besides, HLA-DR mismatches were significant associated with worse overall graft survival (HR: 1.12, 95% CI: 1.05–1.21). For HLA-A locus, the association was insignificant (HR: 1.06; 95% CI: 0.98–1.14). We observed no significant association between HLA-B locus and overall graft failure (HR: 1.01; 95% CI: 0.90–1.15). In subgroup analyses, we found recipient sample size and ethnicity maybe the potential sources of heterogeneity.

Conclusions

HLA mismatching was still a critical prognostic factor that affects graft and recipient survival. HLA-DR mismatching has a substantial impact on recipient’s graft survival. HLA-A mismatching has minor but insignificant impact on graft survival outcomes.

Keywords

  • Human leukocyte antigen
  • Kidney transplantation
  • Graft survival
  • Mortality
  • Meta-analysis

Background

Compared with dialysis, renal transplantation is a more preferred option for end-stage renal disease (ESRD) [1]. In recent report of global database on donation and transplantation (http://www.transplant-observatory.org), about 80,000 renal transplants were performed annually [2]. However, in 2016 United States Renal Data System (USRDS) Annual Data Report, the long-term survival benefit remained unsatisfactory, with ten-year graft survival probabilities of 46.9% for deceased donor transplant [3].

Human leukocyte antigen (HLA) was important biological barrier to a successful transplantation and has substantial impact on the prolongation of graft survival [4]. However, the emergency of modern immunosuppressive agents minimized the effect of HLA compatibility. The US kidney allocation system was extensively modified to eliminated HLA-A similarity in 1995 [5] and HLA-B similarity in 2003 [6]. In the revised United Kingdom kidney allocation scheme, HLA-A matching is no longer considered [7]. But the latest European Renal Best Practice Transplantation Guidelines still recommended that matching of HLA-A, -B, and -DR whenever possible, while gave more weight to HLA-DR locus [8]. So far, the current kidney allocation guideline recommendations were inconsistent in term of HLA compatibility. Besides, for the primary aim to make the kidney last as long as possible, all the current kidney allocation systems were not perfect. Here, we sought to conduct a meta-analysis to assess the magnitude effect of HLA mismatching in adult kidney transplantation, with a particular focus on graft survival and recipient mortality.

Methods

The study was registered in the PROSPERO international prospective register of systematic reviews (CRD42017071894). Details of protocol are described in Additional file 1: Supplemental Methods. The meta-analysis was performed in accordance with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) protocol [9] (Additional file 2: Table S1) and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline [10] (Additional file 3: Table S2).

Literature search strategy

We searched PubMed, EMBASE and the Cochrane Library from their inception to December, 2016, without language restriction. We used the following combinations of Medical Subject Heading (MeSH) terms and corresponding text words: “kidney transplantation”, “renal transplantation”, “human leukocyte antigen”, “HLA” and all possible spellings of “survival”. Further details are described in Additional file 1. Reference lists of articles were manually screened to identify further relevant studies. The literature search was performed independently by two investigators (XMS and XHZ). Differences were resolved by consensus.

Study selection

We included studies that (1) included a study cohort comprising adult post-kidney transplant recipients; (2) were cohort studies/trials reporting associations between HLA mismatching and post-transplant survival outcomes; and (3) provided effect estimates of hazard ratios (HRs) with 95% confidence interval (CIs). Studies reporting data on children or animals or in vitro research were excluded. Besides, reviews, meta-analyses, case reports, case series and technical descriptions with insufficient data or unrelated topics were also excluded. For studies covered overlapping data, we included the most recent and informative one. XMS and XHZ independently screened the titles and abstracts for eligibility. Discrepancies were resolved by consensus.

Outcome measures

Our primary clinical endpoint was overall graft failure; secondary clinical endpoints were death-censored graft failure and all-cause mortality. The European Renal Best Practice Transplantation Guidelines and Kidney Disease: Improving Global Outcomes Guidelines was used to evaluate the incidence of measured outcomes [11, 12].

Data extraction and quality assessment

Data were extracted from predefined protocol, then recorded in a standardized Excel form, including the first author’s name, publication date, study location, study design, cohort size, recipient age, sex distribution, duration, donor source, data source (multi-centered or single-centered), follow-up, unadjusted and adjusted HRs of overall graft failure, death-censored graft failure and all-cause mortality per HLA-mismatch increased, and adjusted covariates in reported multivariable analysis. We contacted libraries abroad or corresponding author of relevant articles by email when detailed data for pooling analysis was unavailable. The methodological quality of included studies was described using the Newcastle-Ottawa Scale. High-quality studies were defined by a score of > 5 points [13]. Disagreements in the scores were resolved by consensus between XMS, XHZ and JD.

Statistical analysis

Hazard ratios (HRs) with corresponding 95% confidence intervals (CIs) were directly retrieved from each study. We chose HRs as the statistic estimates because they correctly reflect the nature of data and account for censoring. Cochran’s Q test and I 2 statistic were applied to assess heterogeneity between studies. The following criteria were used: I 2  < 50%, low heterogeneity; 50–75%, moderate heterogeneity and > 75%, high heterogeneity [14, 15]. When significant heterogeneity was found between studies (P < 0.10 or I 2  > 50%), the effect estimates were calculated using a random-effects model and the DerSimonian-Laird method [16]; otherwise, a fixed-effects model with the Mantel-Haenszel method was used [17]. Subgroup analyses included recipient sample size (≥10,000 vs < 10,000), the nature of data (univariable-unadjusted vs multivariable-adjusted effect estimates), donor source (deceased vs living and deceased), data source (multi-centered vs single-centered) and geographical locations (Europe, North America, Asia and Oceania). A sensitivity analyses was performed by omitting one study at a time and then reanalyzing the data to assess the change in effect estimates. To further explore heterogeneity, a random-effects univariate meta-regression was conducted when at least 10 studies were available. For outcomes of at least 10 studies included, publication bias was assessed by funnel plot and Egger test [18]. Egger test with two-sided P < 0.10 was considered to be statistically significant. Analyses were performed using STATA software, version 13.0 (STATA Corporation, College Station, Texas, USA).

Results

Of 5647 articles identified, we reviewed the full text of 541 reports, and 23 studies [1941] with 486,608 adult post-transplant recipients were included in the meta-analysis (Fig. 1). Detailed characteristics of included studies are presented in Table 1. Among these studies, 18 studies provided multivariable-adjusted effect estimates [19, 2124, 2739], 3 studies provided both multivariable-adjusted and univariable-unadjusted data [20, 25, 26], and 2 studies provided univariable-unadjusted data [40, 41]. Besides, 8 studies were multi-centered [19, 23, 27, 32, 33, 35, 37, 38]; another 15 studies were single-centered [2022, 2426, 2831, 34, 36, 3941]. When considering HLA locus as categories, 11 studies reported survival outcomes of all HLA locus (HLA-A, -B and -DR) [20, 2328, 32, 35, 36, 40], 8 [19, 20, 22, 2931, 33, 41] reported HLA-DR locus, 4 with HLA-B locus [19, 30, 31, 33], and 3 with HLA-A locus [30, 31, 33]. The methodological quality score was high, ranging from 6 to 8 points (details of quality assessment are provided in Additional file 4: Table S3).
Fig. 1
Fig. 1

PRISMA flow chart of study selection

Table 1

Baseline characteristics of the studies included in the meta-analysis

Author, year

No. of recipients

Mean age, years

Male, %

Year data collection

Country of origin

Data Source

Type of risk (Adjustments)

Quality score

De Fijter, 2001 [20]

496

47.4

62.1

1983–1997

Netherlands

Single-center

Recipient and donor age and gender, CIT, PRA, initial immunosuppression, DGF, ARE, type of AR

8

Roodnat, 2003 [21]

1124

44.8

58.5

1981–2000

Netherlands

Single-center

Recipient and donor age, donor gender, CIT, donor Cr, transplantation year, donor type, number of previous transplants

8

Tekin, 2015 [22]

2633

47.7

40.4

2008–2013

Turkey

Single-center

Recipient and donor age and gender, donor follow-up Cr levels, time on dialysis, original disease, CIT, DGF, ARE, recipient serum Cr-levels, warm ischemia times

7

Mandal, 2003 [23]

31,909

NR

59.0

1995–1998

USA

Registry (USRDS)

Recipient and donor age, recipient gender and race, donor type, CIT, diabetic nephropathy

8

Arias, 2007 [24]

214

47.7

64.0

NR

Spain

Single-center

Recipient and donor age and gender, donor type, ARE, CMV, CIT, PRA, glomerulosclerosis, interstitial fibrosis, tubular atrophy, arteriosclerosis, arteriolar hyalinosis

8

Cho, 2016 [25]

229

63.2

63.6

1995–2014

Korea

Single-center

Recipient and donor age, donor type, recipient gender, ABO-incompatible, DGF, CMV, HBV, HCV, time on dialysis prior to transplantation

8

Gomez, 2013 [26]

487

38.0

63.2

1979–1997

Spain

Single-center

Recipient and donor age, donor gender, donor type, DGF, CIT, PRA, AR, time on dialysis, immunosuppression

8

Laging, 2012 [28]

1821

47.8

62.0

1990–2009

Netherlands

Single-center

Recipient and donor age, maximum PRA, current PRA, transplant year, donor gender, donor type, DGF, immunosuppression

8

Laging, 2014 [35]

1998

48.2

62.5

1990–2010

Netherlands

Single-center

Recipient and donor age, donor gender, donor type, PRA, transplant year, immunosuppression

8

Schnuelle, 1999 [30]

152

46.4

56.7

1989–1998

Germany

Single-center

Recipient and donor age, recipient gender, time on dialysis, original disease, PRA, dopamine, noradrenaline, head trauma, previous transplant, immunosuppression, Induction (ATG/OKT3)

8

Hariharan, 2002 [32]

105,742

NR

NR

1988–1998

USA

Registry (OPTN/UNOS)

Recipient and donor age and race, gender, DM, hypertensive nephropathy, pre-TX dialysis and transfusions, previous transplant, most recent PRA, DGF, donor type, 1-year AR, induction therapy, immunosuppression regiment

7

Massie, 2016 [19]

106,019

50.0

62.4

2005–2013

USA

Registry (SRTR)

Recipient and donor age, gender and race, PRA, transplant year, private insurance, HCV, eGFR, BMI, cigarette use, SBP, ABO-incompatible, unrelated to recipient, min(donor/recipient weight ratio,0.9)

8

Cho, 2012 [33]

39,332

52.0

51.2

2000–2008

USA

Registry (OPTN/UNOS)

Recipient and donor age, gender, race, CAD, CVD, DM, PVD, pulmonary, malignancy, CMV, DGF, rejection treatment

8

Croke, 2010 [27]

12,662

NR

NR

1985–2007

Australia/ New Zealand

Registry (ANZDATA)

Donor and recipient variables,transplant year,type of initial CNI

8

Connolly, 1996 [31]

516

NR

67.4

1989–1993

UK

Single-center

Recipient and donor age and gender, donor type, DGF, CIT, PRA, ARE, warm ischemia time,

8

Asderakis, 2001 [29]

788

42.1

67.8

1990–1995

UK

Single-center

Recipient and donor gender, donor age, DGF, CIT, ARE, immunosuppression

8

Opelz, 2007 [35]

135,970

NR

61.6

1985–2004

Germany

Registry (CTS)

Recipient and donor age, gender, and race, PRA, CIT, transplant year, time on dialysis, original disease, previous transplant, pre-transplant dialysis, recipient geographical origin, immunosuppression

8

Amatya, 2010 [36]

229

40.5

59.4

1997–2007

USA

Single-center

Recipient age, gender, and race, BMI, PRA, previous transplant, CIT, WIT

8

Zukowski, 2014 [40]

232

37.7

63.8

1997–1998

Poland

Multi-center

Univariate

6

Fellstrom, 2005 [41]

2102

51

65.2

1996–1997

Europe

Multi-center

Univariate

6

Summers 2010 [37]

9134

47

61.4

2000–2007

UK

Registry (UK transpalnt registry)

Recipient age, donor age, CIT

6

Van 1996 [39]

1289

32.8

60.9

1966–1994

Netherlands

Single-center

Recipient and donor age and gender, type of immnnosuppression, presensitisation, DM, and living or post-mortem donor

8

Lynch 2013 [38]

31,534

N/A

N/A

2000–2010

USA

Registry (SRTR)

Not reported

8

ANZDATA Australia and New Zealand Dialysis and Transplant Registry, AR acute rejection, ARE acute rejection episode, ATG antithymocyte globulin, BMI body mass index, CAD coronary artery disease, CIT cold ischemic time, CMV cytomegalovirus, CNI calcineurin inhibitor, Cr creatinine, CTS Collaborative Transplant Study, CVD cardiovascular disease, DGF delayed graft function, DM diabetes mellitus, GFR glomerular filtration rate, HBV hepatitis B virus, HCV hepatitis C virus, NR not reported, OPTN the Organ Procurement and Transplantation Network, PRA panel reactive antibodies, pre-TX pre-transplant, PVD peripheral vascular disease, SBP systolic blood pressure, SRTR Scientific Registry for Transplant Recipient, UNOS United Network for Organ Sharing, USRDS United States Renal Data System, WIT warm ischemic time

Primary outcomes

HLA per mismatch and overall graft failure

Eleven studies (289,987 adult recipients) reported data on HLA mismatching and overall graft failure. The pooled analysis revealed that each incremental increase of HLA-mismatches was significant associated with a higher risk of overall graft failure, both in univariable-unadjusted summary estimates (HR: 1.14; 95% CI: 1.04–1.26; P = 0.008; Fig. 2) and multivariable-adjusted summary estimates (HR: 1.06; 95% CI: 1.05–1.07; P < 0.001; Fig. 2). The heterogeneity was low (I 2  = 24.8 and 27.4%, respectively). Detailed predefined subgroup analyses were listed in Table 2. The effect estimates did not changed significantly after stratification for sample size (≥10,000 vs < 10,000), data source (multi-centered vs single-centered), donor source (cadaveric vs living and cadaveric), geographic locations (European, North America, Asia and Oceania) and year period (prior to 1995 vs not prior to 1995). In sensitivity analysis, the summary estimates were not modified after excluding one study at a time. Subsequent univariate meta-regression indicated that these factors did not significantly change the overall effect (Additional file 5: Fig. S1). Publication bias was not significant (Additional file 6: Fig. S2).
Fig. 2
Fig. 2

Forest plots of the association between HLA per mismatch and overall graft failure, using both of univariable-unadjusted and multivariable-adjusted effect estimates

Table 2

Subgroup analyses of overall graft failure associated with HLA per mismatch and HLA-DR mismatches

 

HLA per mismatch

HLA-DR mismatches

Subgroup

No. of recipients (cohorts)

HR (95% CI)

I 2

P a

No. of recipients (cohorts)

HR (95% CI)

I 2

P a

Sample size

   

0.835

   

0.011

 ≥10,000

281,141 (5)

1.06 (1.05–1.07)

49.1

 

145,351 (2)

1.07 (1.04–1.10)

0

 

 < 10,000

8614 (7)

1.06 (1.03–1.09)

17.3

 

4652 (5)

1.27 (1.12–1.43)

48.4

 

Nature of data

   

0.133

   

0.076

 Univariable-unadjusted

1440 (4)

1.14 (1.04–1.26)

24.8

 

2598 (2)

1.39 (1.05–1.83)

70.0

 

 Multivariable-adjusted

286,755 (12)

1.06 (1.05–1.07)

27.4

 

150,003 (7)

1.08 (1.05–1.11)

58.3

 

Data source

   

0.683

   

0.011

 Registry/Multi-center

286,283 (6)

1.06 (1.05–1.07)

38.9

 

145,351 (2)

1.07 (1.04–1.10)

0

 

 Single-center

3472 (6)

1.07 (1.02–1.12)

26.6

 

4652 (5)

1.27 (1.12–1.43)

48.4

 

Donor source

   

0.027

   

0.616

 Cadaveric

138,516 (5)

1.07 (1.06–1.07)

0.00

 

41,284 (5)

1.08 (1.04–1.11)

66.4

 

 Living and Cadaveric

151,239 (7)

1.05 (1.05–1.06)

16.4

 

108,719 (2)

1.09 (1.04–1.15)

55.4

 

 Geographical locations

   

0.053

   

0.033

 Europe

138,988 (6)

1.07 (1.06–1.08)

0.00

 

1952 (4)

1.32 (1.09–1.60)

59.7

 

 North America

137,880 (4)

1.05 (1.04–1.06)

2.1

 

145,351 (2)

1.07 (1.04–1.10)

0

 

 Asia

225 (1)

1.24 (1.00–1.55)

 

2700 (1)

1.23 (1.05–1.45)

 

 Oceania

12,662 (1)

1.07 (1.03–1.12)

 

0

 

Year period

   

0.175

   

0.026

 Prior to 1995

103,915 (6)

1.06 (1.05–1.07)

0.00

 

148,051 (3)

1.08 (1.04–1.12)

26.8

 

 Not prior to 1995

185,626(5)

1.06 (1.05–1.08)

60.1

 

4054 (4)

1.36 (0.98–1.88)

59.7

 

 NR

214 (1)

0.83 (0.51–1.34)

The effect estimates were stratified for sample size (≥10,000 vs < 10,000), data source (multi-centered vs single-centered), donor source (cadaveric vs living and cadaveric), geographic locations (European, North America, Asia and Oceania) and year period (prior to 1995 vs not prior to 1995)

aP value for heterogeneity

HLA-DR mismatches and overall graft failure

Eight studies with 152,105 adult recipients were analyzed to investigate the association between HLA-DR mismatching and overall graft failure. The pooled results revealed an unadjusted HR of 1.44 (95% CI: 0.86–2.41; P = 0.160) with moderate heterogeneity (I 2  = 70.0%). After adjustment, each incremental increase of HLA-DR mismatches was significant associated with 12% higher risk of overall graft failure (HR: 1.12; 95% CI: 1.05–1.21; P = 0.002; Fig. 3), with moderate heterogeneity (I 2  = 58.3%). Sensitivity analysis with a fixed-effects model obtained similar results (HR: 1.08; 95% CI: 1.05–1.11; P < 0.001). Subsequent subgroup analysis demonstrated that the effect was not modified after stratification for sample size, data source, donor source and geographical locations (Table 2). The effect estimates remained stable after excluding one study at a time. Considering only 8 studies included in meta-analysis, we did not perform a meta-regression.
Fig. 3
Fig. 3

Forest plots of the association between HLA-A, -B, -DR mismatches and overall graft failure

In addition, three studies with 41,957 recipients evaluated 1 or 2 DR-mismatches versus 0 DR-mismatches. Compared with 0 mismatches in HLA-DR antigen, 1 mismatches and 2 mismatches were all associated with higher risk of overall graft failure, with pooled HRs of 1.12 (95% CI: 1.04–1.21; P = 0.002) and 1.15 (95% CI: 1.05–1.25; P = 0.002), respectively (Additional file 7: Fig. S3). In both pooled analysis, there was no heterogeneity (I 2  = 0%).

HLA-B mismatches and overall graft failure

Associations of HLA-B epitope and overall graft failure were reported in 4 studies with 146,019 recipients. The pooled analysis demonstrated that each incremental increase of HLA-B mismatches was not associated with higher risk of overall graft failure (HR: 1.01; 95% CI: 0.90–1.15; P = 0.834; Fig. 3), with moderate heterogeneity (I 2  = 66.0%). Sensitivity analysis with a fixed-effects model obtained similar effect estimates (HR: 1.01; 95% CI: 0.89–1.14; P = 0.079). In addition, the effect estimates did not changed significantly after stratification for sample size (≥10,000 vs < 10,000) of cohorts.

HLA-A mismatches and overall graft failure

Only 3 studies (40,000 recipients) reported data on the association of HLA-A epitope and overall graft failure. The results revealed an insignificant association (HR: 1.06; 95% CI: 0.98–1.14; P = 0.121; Fig. 3), with no heterogeneity (I 2  = 0%). Sensitivity analysis with a random-effects model showed similar results (HR: 1.06; 95% CI: 0.98–1.15; P = 0.121). The results should be cautiously interpreted because of only three studies included.

Secondary outcomes

Death-censored graft failure

We included 101,093 recipients from 4 cohorts. Each incremental increase of HLA mismatches was associated with a higher risk of death-censored graft failure, with summary HR of 1.09 (95% CI: 1.06–1.12; P < 0.001; Fig. 4), and moderate heterogeneity (I 2  = 70.9%). Sensitivity analysis with a fixed-effects model showed similar results (HR: 1.09; 95% CI: 1.08–1.10; P < 0.001). The summary estimates were not modified after including only large sample size of cohorts (> 10,000 recipients) (Additional file 8: Fig. S4).
Fig. 4
Fig. 4

Forest plots of the association between HLA per mismatch and death-censored graft failure

All-cause mortality

We included 180,766 recipients from 4 cohorts. Each incremental increase of HLA mismatches was associated with a higher risk of all-cause mortality rates (HR: 1.04; 95% CI: 1.02–1.07; P = 0.001; Fig. 5). The heterogeneity was moderate (I 2  = 65.3%). Summary estimates did not changed significantly after analyzing with a fixed-effects model (HR: 1.04; 95% CI: 1.02–1.07; P = 0.001). After stratification for sample size of cohorts (≥10,000 vs < 10,000), the effect estimates were not modified (HR: 1.04; 95% CI: 1.02–1.05; P < 0.001; I 2  = 27.8%, Additional file 8: Fig. S4). However, the results should be cautiously interpreted due to small number of included studies (n = 4).
Fig. 5
Fig. 5

Forest plots of the association between HLA per mismatch and all-cause mortality

Discussion

This is the first meta-analysis to evaluate the magnitude effect of HLA mismatching on post-transplant survival outcomes of adult kidney transplantation. The analysis included 23 studies with a large sample of subjects (totally 486,608 recipients). The results indicated that each incremental increase of HLA mismatches was significantly associated with higher risks of overall graft failure, death-censored graft failure and all-cause mortality. The pooled results also indicated that HLA-DR mismatches were significantly associated with a 12% higher risk of overall graft failure. We also observed that HLA-A per mismatch was associated with a 6% higher risk of overall graft failure, but the association was insignificant. There was no significant association between HLA-B mismatching and graft survival. All included studies were in high methodological quality and the heterogeneity between studies was acceptable in each pooling analysis. In addition, we found that sample size or recipient ethnicity may be potential sources of heterogeneity.

Human HLA genes are located on chromosome 6 and code for 3 major class I alleles (HLA-A, -B, -C) and 3 major class II alleles (HLA-DR, -DQ, -DP). Polymorphisms in HLA, especially HLA-A, -B, and -DR loci, are important biological barriers to a successful transplantation [42, 43]. As closely HLA-matched graft is less likely to be recognized and rejected, HLA mismatching has a substantial impact on prolongation of graft survival. With the emergence of potent immunosuppressive agents that steadily improved the graft survival rates, the impact of HLA compatibility seems to be minimized [42, 44]. But the recent Australia and New Zealand Dialysis and Transplant Registry (ANZDTR) survey with 12,662 recipients still demonstrated that each incremental increase of HLA mismatches was significantly associated with higher risk of graft failure and rejection [27]. Another recent survey from Massie et al. [19] with 106,019 recipients from the Scientific Registry for Transplant Recipients (SRTR) database revealed that HLA-B and -DR mismatches were all significant associated with worse graft survival outcomes. Using multivariable-adjusted data (adjusting for other determinant confounders such as donor and recipient age, gender, combined disease, serum creatinine levels, ischemic times, etc.), the present analysis indicated that HLA per mismatch was associated with an increased risk of overall graft failure (9%), death-censored graft failure (6%) and all-cause mortality (4%). The pooled results were in favor of recommendations of the latest European Renal Best Practice Transplantation Guidelines, which recommended that matching of HLA-A, -B, and -DR whenever possible [8].

The meta-analysis suggested that HLA-DR per mismatch was significant associated with a 12% higher risk of overall graft failure. Besides, a subsequent analysis suggested that compared with 0 DR-mismatches, 1 and 2 mismatches were significant associated with 12 and 15% higher risk of overall graft failure, respectively. The pooled results were in favor of the kidney allocation guideline recommendations in almost all countries, such as the current US kidney allocation system, the revised United Kingdom kidney allocation scheme, and the latest European Renal Best Practice Transplantation Guidelines, which all highlighted the importance of HLA-DR testing [58].

Notably, the present analysis revealed a tendency that HLA-A mismatching had an impact on overall graft survival as there were only 3 studies included with a pooled HR of 1.06 (95% CI: 0.98–1.14). However, we did not observe a significant association between HLA-B mismatching and overall graft survival (HR: 1.01; 95% CI: 0.90–1.15). Our pooled results were inconsistent with the recommendations of the revised United Kingdom kidney allocation scheme, which eliminated the impact of HLA-A similarity instead of HLA-B similarity [7]. Moreover, miscellaneous factors can result in inferior outcomes [45]. For instance, inferior graft outcomes could be related to high risk for rejection particularly antibody-mediated rejection [4547]. Inferior patient survival could partly be associated with consequences of enhanced immunosuppression [45]. Consequently, the pooled results should be cautiously interpreted and further studies should be conducted to investigate the impact of HLA-A mismatching on graft and recipient survival outcomes.

Subgroup analysis and meta-regression was conducted to explore heterogeneity between studies. In subgroup analysis of the association between HLA per mismatch and overall graft failure, we found that after stratification for donor source (cadaveric vs living and cadaveric), the heterogeneity decreased to insignificant (I 2  = 0 and 16.4, respectively). But subsequent meta-regression analysis revealed that donor source did not change the overall effect significantly. In subgroup analysis of the association between HLA-DR mismatching and overall graft failure, we found that ethnicity and recipient sample size were potential source of heterogeneity. Large sample size of cohorts usually demonstrated more stable results. Besides, ethnic diversity was a potential source of heterogeneity probably because of varying HLA polymorphisms in the genetic makeup of the geographically distinct cohorts.

Strengths and limitations

Strengths of our meta-analysis are large sample of subjects (totally 486,608 recipients) and strict study design. Besides, we used multivariable-adjusted data for pooling analysis, which adjusted for some primary determinant confounders. However, the present meta-analysis had some limitations. Firstly, the absence of randomized controlled trials was the biggest limitation of this meta-analysis. Secondly, several studies have suggested that other HLA loci, such as HLA-C and -DQ locus, may contribute to poorer graft outcomes [4850], but this meta-analysis only included the HLA-A, -B and -DR loci. Thirdly, heterogeneity is inevitable in some outcomes. We conducted several subgroup and meta-regression analyses to explore the potential source of heterogeneity, and used random-effects models to incorporate heterogeneity between studies. Fourthly, few studies included could provide data about induction agent, maintenance agent or PRA, so that it cannot be achieved to do the stratified analysis.

Conclusions

HLA mismatching was still a critical prognostic factor that affects graft and recipient survival. HLA-DR mismatching has a substantial impact on recipient’s graft survival. HLA-A mismatching has minor but not significant impact on graft survival outcomes. Further studies should be conducted to confirm the impact of HLA-A similarity.

Abbreviations

ANZDATA: 

Australia and New Zealand Dialysis and Transplant Registry

AR: 

Acute rejection

ARE: 

Acute rejection episode

ATG: 

Antithymocyte globulin

BMI: 

Body mass index

CAD: 

Coronary artery disease

CI: 

Confidence interval

CIT: 

Cold ischemic time

CMV: 

Cytomegalovirus

CNI: 

Calcineurin inhibitor

Cr: 

Creatinine

CTS: 

Collaborative Transplant Study

CVD: 

Cardiovascular disease

DGF: 

Delayed graft function

DM: 

Diabetes mellitus

ESRD: 

End-stage renal disease

GFR: 

Glomerular filtration rate

HBV: 

Hepatitis B virus

HCV: 

Hepatitis C virus

HLA: 

Human leukocyte antigen

HR: 

Hazard ratio

NR: 

Not reported

OPTN: 

The Organ Procurement and Transplantation Network

PRA: 

Panel reactive antibodies

pre-TX: 

Pre-transplant

PVD: 

Peripheral vascular disease

SBP: 

Systolic blood pressure

SRTR: 

The Scientific Registry for Transplant Recipient

UNOS: 

The United Network for Organ Sharing

USRDS: 

The United States Renal Data System

WIT: 

Warm ischemic time

Declarations

Funding

The design of the study was supported by Beijing key laboratory of molecular diagnosis and study on pediatric genetic diseases (No. BZ0317). The collection, analysis, and interpretation of data were supported by the National Key Research and Development Program of China, the registry of rare diseases in children (No. 2016YFC0901505).

Availability of data and materials

All data generated or analyzed during this study are included in this article and its additional files.

Authors’contributions

All authors take responsibility for the integrity of the data and the accuracy of data analysis. Study concept and design: JD. Extraction, analysis and interpretation of data: XS and XZ. Drafting of the manuscript: XS. Critical revision of the manuscript: JD, JL, XZ and WH. Statistical analysis: XS, JL, XX and BS. Technical support: JL and WH. All authors have read and agreed to the submission to this journal of the manuscript.

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Pediatrics, Peking University First Hospital, Beijing, China
(2)
Renal Division, Peking University First Hospital, Beijing, China
(3)
Peking University Institute of Nephrology, Beijing, China
(4)
Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, China
(5)
Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking Unversity, Ministry of Education, Beijing, China
(6)
Institute of Urology, Peking University, Beijing, China
(7)
Department of Urology, Peking University First Hospital, Beijing, China

References

  1. Ferrari P, Weimar W, Johnson RJ, Lim WH, Tinckam KJ. Kidney paired donation: principles, protocols and programs. Nephrol Dial Transplant. 2015;30(8):1276–85.View ArticlePubMedGoogle Scholar
  2. Global Observatory on Donation and Transplantation, World Health Organization. Organ Donation and Transplantation Activities 2014. http://www.transplant-observatory.org/data-reports-2014/ Assessed Apr 2016.
  3. Ai Dhaybi O, Bakris GL. Renal targeted therapies of antihypertensive and cardiovascular drugs for patients with stages 3 through 5d kidney disease. Clin Pharmacol Ther. 2017;102(3):450–8.View ArticlePubMedGoogle Scholar
  4. Al-Otaibi T, Gheith O, Mosaad A, Nampoory MR, Halim M, Said T, et al. Human leukocyte antigen-DR mismatched pediatric renal transplant: patient and graft outcome with different kidney donor sources. Exp Clin Transplant. 2015;13(Suppl 1):117–23.PubMedGoogle Scholar
  5. Ashby VB, Port FK, Wolfe RA, Wynn JJ, Williams WW, Roberts JP. Transplanting kidneys without points for HLA-B matching: consequences of the policy change. Am J Transplant. 2011;11(8):1712–8.View ArticlePubMedGoogle Scholar
  6. Leffell MS, Zachary AA. The national impact of the 1995 changes to the UNOS renal allocation system. United network for organ sharing. Clin Transpl. 1999;13(4):287–95.View ArticleGoogle Scholar
  7. Johnson RJ, Fuggle SV, O'Neill J, Start S, Bradley JA, Forsythe JL, et al. Factors influencing outcome after deceased heart beating donor kidney transplantation in the United Kingdom: an evidence base for a new national kidney allocation policy. Transplantation. 2010;89(4):379–86.View ArticlePubMedGoogle Scholar
  8. Abramowicz D, Cochat P, Claas FH, Heemann U, Pascual J, Dudley C, et al. European renal best practice guideline on kidney donor and recipient evaluation and perioperative care. Nephrol Dial Transplant. 2015;30(11):1790–7.View ArticlePubMedGoogle Scholar
  9. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of observational studies in epidemiology (MOOSE) group. JAMA. 2000;283(15):2008–12.View ArticlePubMedGoogle Scholar
  10. Moher D, Liberati A, Tetzlaff J. Altman DG. PRISMA Group Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement BMJ. 2009;339:b2535.PubMedGoogle Scholar
  11. European Renal Best Practice Transplantation Guideline Development Group. ERBP guideline on the management and evaluation of the kidney donor and recipient. Nephrol Dial Transplant. 2013;28(Suppl 2):i1–i71.View ArticleGoogle Scholar
  12. Kidney Disease. Improving Global Outcomes (KDIGO) Transplant Work Group. KDIGO clinical practice guideline for the care of kidney transplant recipients. Am J Transplant. 2009;9(Suppl 3):s1–s155.Google Scholar
  13. Wells GA, Shea B, O’Connell D, Peterson G, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Ottawa, Ontario: Ottawa Hospital Research Institute. 2013. http://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Assessed 15 Dec 2015.
  14. Deeks JJ, Higgins JPT, Altman DG. Chapter 9: Analysing data and undertaking meta-analyses. In: Higgins JPT, Green S, editors. Cohrane handbook for sustematic reviews of interventions. Version 5.1.0, The Cochrane Collaboration. 2011. http://training.cochrane.org/handbook. Accessed Mar 2011.
  15. Cheng YJ, Nie XY, Chen XM, Lin XX, Tang K, Zeng WT, et al. The role of macrolide antibiotics in increasing cardiovascular risk. J Am Coll Cardiol. 2015;66(20):2173–84.View ArticlePubMedGoogle Scholar
  16. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.View ArticlePubMedGoogle Scholar
  17. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22(4):719–48.PubMedGoogle Scholar
  18. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Massie AB, Leanza J, Fahmy LM, Chow EK, Desai NM, Luo X, et al. A risk index for living donor kidney transplantation. Am J Transplant. 2016;16(7):2077–84.View ArticlePubMedPubMed CentralGoogle Scholar
  20. de Fijter JW, Mallat MJ, Doxiadis II, Ringers J, Rosendaal FR, Claas FH, et al. Increased immunogenicity and cause of graft loss of old donor kidneys. J Am Soc Nephrol. 2001;12(7):1538–46.PubMedGoogle Scholar
  21. Roodnat JI, van Riemsdijk IC, Mulder PG, Doxiadis I, Claas FH, IJzermans JN, et al. The superior results of living-donor renal transplantation are not completely caused by selection or short cold ischemia time: a single-center, multivariate analysis. Transplantation. 2003;75(12):2014–8.View ArticlePubMedGoogle Scholar
  22. Tekin S, Yavuz HA, Yuksel Y, Yucetin L, Ateş I, Tuncer M, et al. Et al. kidney transplantation from elderly donor. Transplant Proc. 2015;47(5):1309–11.View ArticlePubMedGoogle Scholar
  23. Mandal AK, Snyder JJ, Gilbertson DT, Collins AJ, Silkensen JR. Does cadaveric donor renal transplantation ever provide better outcomes than live-donor renal transplantation? Transplantation. 2003;75(4):494–500.View ArticlePubMedGoogle Scholar
  24. Arias LF, Blanco J, Sanchez-Fructuoso A, Prats D, Duque E, Sáiz-Pardo M, et al. Histologic assessment of donor kidneys and graft outcome: multivariate analyses. Transplant Proc. 2007;39(5):1368–70.View ArticlePubMedGoogle Scholar
  25. Cho H, Yu H, Shin E, Kim YH, Park SK, Jo MW. Risk factors for graft failure and death following geriatric renal transplantation. PLoS One. 2016;11(4):e0153410.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Gómez EG, Hernández JP, López FJ, Garcia JR, Montemayor VG, Curado FA, et al. Long-term allograft survival after kidney transplantation. Transplant Proc. 2013;45(10):3599–602.View ArticlePubMedGoogle Scholar
  27. Croke R, Lim W, Chang S, Campbell S, Chadban S, Russ G, et al. HLA-mismatches increase risk of graft failure in renal transplant recipients initiated on cyclosporine but not tacrolimus. Nephrology. 2010;15:38.Google Scholar
  28. Laging M, Kal-van Gestel JA, van de Wetering J, Ijzermans JN, Weimar W, Roodnat JI. The relative importance of donor age in deceased and living donor kidney transplantation. Transpl Int. 2012;25(11):1150–7.View ArticlePubMedGoogle Scholar
  29. Asderakis A, Dyer P, Augustine T, Worthington J, Campbell B, Johnson RW. Effect of cold ischemic time and HLA matching in kidneys coming from "young" and "old" donors: do not leave for tomorrow what you can do tonight. Transplantation. 2001;72(4):674–8.View ArticlePubMedGoogle Scholar
  30. Schnuelle P, Lorenz D, Mueller A, Trede M, Van Der Woude FJ. Donor catecholamine use reduces acute allograft rejection and improves graft survival after cadaveric renal transplantation. Kidney Int. 1999;56(2):738–46.View ArticlePubMedGoogle Scholar
  31. Connolly JK, Dyer PA, Martin S, Parrott NR, Pearson RC, Johnson RW. Importance of minimizing HLA-DR mismatch and cold preservation time in cadaveric renal transplantation. Transplantation. 1996;61(5):709–14.View ArticlePubMedGoogle Scholar
  32. Hariharan S, McBride MA, Cherikh WS, Tolleris CB, Bresnahan BA, Johnson CP. Post-transplant renal function in the first year predicts long-term kidney transplant survival. Kidney Int. 2002;62(1):311–8.View ArticlePubMedGoogle Scholar
  33. Cho YW, Lemp N, Shah T, Sampaio MS, Hutchinson IV, Kasahara A, et al. Long-term risk factors of kidney graft survival in the modern immunosuppression era. Am J Transplant. 2012;12:490.Google Scholar
  34. Laging M, Kal-van Gestel JA, Haasnoot GW, Claas FH, van de Wetering J, Ijzermans JN, et al. Transplantation results of completely HLA-mismatched living and completely HLA-matched deceased-donor kidneys are comparable. Transplantation. 2014;97(3):330–6.View ArticlePubMedGoogle Scholar
  35. Opelz G, Döhler B. Effect of human leukocyte antigen compatibility on kidney graft survival: comparative analysis of two decades. Transplantation. 2007;84(2):137–43.View ArticlePubMedGoogle Scholar
  36. Amatya A, Florman S, Paramesh A, Amatya A, Mcgee J, Killackey M, et al. HLA-matched kidney transplantation in the era of modern immunosuppressive therapy. Dial Transplant. 2010;39(5):193–8.View ArticleGoogle Scholar
  37. Summers DM, Johnson RJ, Allen J, Fuggle SV, Collett D, Watson CJ, et al. Analysis of factors that affect outcome after transplantation of kidneys donated after cardiac death in the UK: a cohort study. Lancet. 2010;376(9749):1303–11.View ArticlePubMedGoogle Scholar
  38. Lynch S, Tinckam K, Kim J. Increasing HLA DR mismatches is associated with inferior kidney allograft outcome in low immune risk living donor kidney transplant recipients. Am J Transplant. 2013;13:420.View ArticleGoogle Scholar
  39. van Saase JL, Mallat MJ, van der Woude FJ, van Bockel JH, van Es LA. Results of renal transplantation in Leiden, 1966-1994, and prognostic factors. Ned Tijdschr Geneeskd. 1996;140(15):827–32.PubMedGoogle Scholar
  40. Zukowski M, Kotfis K, Kaczmarczyk M, Biernawska J, Szydłowski L, Zukowska A, et al. Influence of selected factors on long-term kidney graft survival-a multivariable analysis. Transplant Proc. 2014;46(8):2696–8.View ArticlePubMedGoogle Scholar
  41. Fellström B, Holdaas H, Jardine AG, Nyberg G, Grönhagen-Riska C, Madsen S, et al. Risk factors for reaching renal endpoints in the assessment of Lescol in renal transplantation (ALERT) trial. Transplantation. 2005;79(2):205–12.View ArticlePubMedGoogle Scholar
  42. Broeders N, Racapé J, Hamade A, Massart A, Hoang AD, Mikhalski D, et al. A new HLA allocation procedure of kidneys from deceased donors in the current era of immunosuppression. Transplant Proc. 2015;47(2):267–74.View ArticlePubMedGoogle Scholar
  43. Laperrousaz S, Tiercy S, Villard J, Ferrari-Lacraz S. HLA and non-HLA polymorphisms in renal transplantation. Swiss Med Wkly. 2012;142:w13668.PubMedGoogle Scholar
  44. Süsal C, Opelz G. Current role of human leukocyte antigen matching in kidney transplantation. Curr Opin Organ Transplant. 2013;18(4):438–44.View ArticlePubMedGoogle Scholar
  45. Legendre C, Canaud G, Martinez F. Factors influencing long-term outcome after kidney transplantation. Transpl Int. 2014;27(1):19–27.View ArticlePubMedGoogle Scholar
  46. Wu K, Budde K, Schmidt D, Neumayer HH, Lehner L, Bamoulid J, et al. The inferior impact of antibody-mediated rejection on the clinical outcome of kidney allografts that develop de novo thrombotic microangiopathy. Clin Transpl. 2016;30(2):105–17.View ArticleGoogle Scholar
  47. Nin M, Coitiño R, Kurdian M, Orihuela L, Astesiano R, Garau M, et al. Acute antibody-mediated rejection in kidney transplant based on the 2013 Banff criteria: single-center experience in Uruguay. Transplant Proc. 2016;48(2):612–5.View ArticlePubMedGoogle Scholar
  48. Wiebe C, Pochinco D, Blydt-Hansen TD, Ho J, Birk PE, Karpinski M, et al. Class II HLA epitope matching-a strategy to minimize de novo donor-specific antibody development and improve outcomes. Am J Transplant. 2013;13(12):3114–22.View ArticlePubMedGoogle Scholar
  49. Kosmoliaptsis V, Gjorgjimajkoska O, Sharples LD, Chaudhry AN, Chatzizacharias N, Peacock S, et al. Impact of donor mismatches at individual HLA-A, -B, -C, -DR, and -DQ loci on the development of HLA-specific antibodies in patients listed for repeat renal transplantation. Kidney Int. 2014;86(5):1039–48.View ArticlePubMedGoogle Scholar
  50. DeVos JM, Gaber AO, Knight RJ, Land GA, Suki WN, Gaber LW, et al. Donor-specific HLA-DQ antibodies may contribute to poor graft outcome after renal transplantation. Kidney Int. 2012;82(5):598–604.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement