# Donor genetic burden for cerebrovascular risk and kidney transplant outcome

Kane E. Collins<sup>1,2,3</sup> · Edmund Gilbert<sup>1,2</sup> · Vincent Mauduit<sup>4</sup> · Katherine A. Benson<sup>1,2</sup> · Elhussein A. E. Elhassan<sup>5,6</sup> · Conall O'Seaghdha<sup>5,6</sup> · Claire Hill<sup>7</sup> · Amy Jayne McKnight<sup>7</sup> · Alexander P. Maxwell<sup>7</sup> · Peter J. van der Most<sup>8</sup> · Martin H. de Borst<sup>9</sup> · Weihua Guan<sup>10</sup> · Pamala A. Jacobson<sup>11</sup> · Ajay K. Israni<sup>11</sup> · Brendan J. Keating<sup>12</sup> · Graham M. Lord<sup>13</sup> · Salla Markkinen<sup>14</sup> · Ilkka Helanterä<sup>15</sup> · Kati Hyvärinen<sup>14</sup> · Jukka Partanen<sup>14</sup> · Stephen F. Madden<sup>16</sup> · Sophie Limou<sup>4</sup> · Gianpiero L. Cavalleri<sup>1,2,3</sup> · Peter J. Conlon<sup>5,6</sup>

Received: 20 December 2023 / Accepted: 26 April 2024
© The Author(s) 2024

## Abstract

**Background and hypothesis** Kidney grafts from donors who died of stroke and related traits have worse outcomes relative to grafts from both living donors and those who died of other causes. We hypothesise that deceased donors, particularly those who died of stroke, have elevated polygenic burden for cerebrovascular traits. We further hypothesise that this donor polygenic burden is associated with inferior graft outcomes in the recipient.

**Methods** Using a dataset of 6666 deceased and living kidney donors from seven different European ancestry transplant cohorts, we investigated the role of polygenic burden for cerebrovascular traits (hypertension, stroke, and intracranial aneurysm (IA)) on donor age of death and recipient graft outcomes.

**Results** We found that kidney donors who died of stroke had elevated intracranial aneurysm and hypertension polygenic risk scores, compared to healthy controls and living donors. This burden was associated with age of death among donors who died of stroke. Increased donor polygenic risk for hypertension was associated with reduced long term graft survival (HR:

Gianpiero L. Cavalleri and Peter J. Conlon contributed equally.

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Peter J. Conlon
peterconlon@beaumont.ie

1 School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland

2 The Science Foundation Ireland FutureNeuro Centre of Excellence, Dublin, Ireland

3 SFI Centre for Research Training in Genomics Data Science, University of Galway, Galway, Ireland

4 Center for Research in Transplantation and Translational Immunology, UMR1064, Nantes University, Ecole Centrale Nantes, INSERM, Nantes, France

5 Department of Nephrology and Transplantation, Beaumont Hospital, Dublin, Ireland

6 Department of Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland

7 Centre for Public Health, Queen's University Belfast, Belfast, United Kingdom

8 Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

9 Department of Internal Medicine, Division of Nephrology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

10 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA

11 College of Pharmacy, University of Minnesota, Minneapolis, Minnesota, USA

12 Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

13 Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom

14 Finnish Red Cross Blood Service, Research and Development, Biomedicum 1, Helsinki, Finland

15 Transplantation and Liver Surgery, Helsinki University Hospital, Helsinki, Finland

16 Data Science Centre, Royal College of Surgeons in Ireland, Beaux Lane House, Dublin, Ireland
1.44, 95% CI [1.07, 1.93]) and increased burden for hypertension, and intracranial aneurysm was associated with reduced recipient estimated glomerular filtration rate (eGFR) at 1 year.

**Conclusions** Collectively, the results presented here demonstrate the impact of inherited factors associated with donors' death on long-term graft function.

**Graphical Abstract**

Donor genetic burden for cerebrovascular risk and kidney transplant outcome

Collins et al, 2024

**Background**

Grafts from deceased donors who died of stroke have worse outcomes than those from donors who died from other causes.

PRS for various traits have shown association with transplant outcome.

**Methods**

6,666 genotyped transplant pairs from 13 centers.

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Calculated donor PRS for hypertension, IA, and stroke

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$$ Score = \sum_{k=1}^{n} (\beta_k * SNP_k) $$

PRS compared between donor types

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Donors who died of stroke have elevated burden for IA and Hypertension PRS compared to healthy controls, living donors and donors who died of other causes

**Results**

Recipient eGFR at 1 year post-transplant

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Bottom decile of PRS

Top decile of PRS

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Hypertension PRS

56.0 ml/min/1.73m² P = 0.001

52.5 ml/min/1.73m²

IA PRS

57.1 ml/min/1.73m² P = 0.008

52.0 ml/min/1.73m²

Median recipient eGFR at 1 year post-transplant compared between top and bottom deciles of polygenic risk for each trait

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Journal of NEPHROLOGY

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official journal of the Italian Society of Nephrology

Donor polygenic burden may explain some of the differences in graft outcome between living and deceased donors, and could potentially be incorporated into algorithms for kidney allocation.

**Keywords** Donors · Genetics · Polygenic risk scores · Post-transplant eGFR · Stroke

**Abbreviations**

CI Confidence interval

DeKAF Deterioration of kidney allograft function

eGFR Estimated glomerular filtration rate

FRCBS Finnish red cross blood service

GWAS Genome-wide association study

HLA Human leukocyte antigen

IA Intracranial aneurysm

IS Ischaemic stroke

KiT-GENIE Kidney transplantation-genomic investigation of essential clinical concerns

PRS Polygenic risk score

QUB Queen's University Belfast

SD Standard deviation

SNP Single nucleotide polymorphism

TL Transplant lines

UKIRTC United Kingdom and Ireland Renal Transplant consortium

**Introduction**

Kidney transplant outcomes are influenced by factors including donor age and sex, whether the donor is living or deceased, clinical era of transplant, donor cause of death, and HLA mismatch [1, 2]. It is well established that organs from living donors generally have superior outcomes compared to those from deceased donors [3]. Further, grafts from deceased donors who died of spontaneous intracranial haemorrhage or stroke have worse outcomes than those from donors who died of other causes, such as trauma [4, 5]. Understanding the factors underlying the poor performance associated with stroke organs could help improve transplant outcomes.

Hypertension is the most prevalent clinical risk factor for stroke and intracranial aneurysm (IA) [6]. Hypertension, intracranial aneurysm and stroke all are highly genetic and have undergone genome-wide association studies (GWAS) [7–9], which have identified multiple
risk loci, each study explaining up to 22% of SNP-based
heritability.

Polygenic Risk Scores (PRSs) can be leveraged to quantify individual genetic burden for a trait using summary statistics from genome-wide association studies. Polygenic risk scores estimate the cumulative effect of common genetic variation on an individual’s disease status weighted by estimated effect size [10, 11]. In the case of ischaemic stroke (IS), previous work has demonstrated that when combined with clinical risk factors, an ischaemic stroke polygenic risk score has the potential to significantly improve risk classification [12]. In addition, intracranial aneurysm polygenic risk score has been shown to predict aneurysmal subarachnoid haemorrhage [13].

Previous studies have demonstrated the impact of both
donor and recipient polygenic risk score on transplant out-
come. For example, donor and recipient polygenic burden
for estimated Glomerular Filtration Rate (eGFR) has been
correlated with transplant outcome. Recipient burden for
skin cancer has also been associated with skin cancer post-
transplant [14, 15]. Other studies [16, 17] have shown an
association between donor genetic risk scores in interleu-
kin-6 and biopsy-proven rejection.

Given the poor transplant outcome associated with
deceased and stroke-related organs in particular, here we
investigate the influence of donor polygenic burden for
stroke, intracranial aneurysm, and hypertension on donor
age of death and kidney transplant outcome. We hypoth-
esized that the polygenic burden of kidney donors is sig-
nificantly different between living and deceased donors, and
between deceased donors who died from stroke and from
other causes. We further hypothesized that differences in
donor burden would manifest in different transplant out-
comes as measured by eGFR and graft survival.

Methods

Ethics statement

The study was approved by the relevant ethics committees
at each site (Data Characteristics section of Supplementary
Materials).

Study design

We hypothesized that the polygenic burden of kidney donors is significantly different between living and deceased donors, and between deceased donors who died from stroke and from other causes. To test this, we calculated the polygenic risk score for hypertension, intracranial aneurysm, and stroke across 6666 kidney donors and 2870 healthy controls from seven cohorts of European ancestry. Polygenic risk score was

compared between healthy controls, living donors, donors
who died of stroke, and donors who died of other causes.
We investigated the impact of these polygenic risk scores on
donor age of death, meta-analysing across all the 7 cohorts.
To test the hypothesis that differences in donor burden would
manifest in different transplant outcomes, we investigated
the impact of polygenic risk score on graft outcomes through
a meta-analysis across the same seven cohorts.

Cohort characteristics

We assembled seven European ancestry cohorts of paired
kidney transplant donors and recipients. Each cohort had
phenotype data pertaining to donor type, donor cause of
death, and transplant outcomes, including eGFR at 1- and
5-years post-transplant. These cohorts included 3 types of
donors: (1) living donors, (2) deceased donors that died of
stroke and intracranial haemorrhage (henceforth referred to
as donors that died of stroke), and (3) deceased donors that
died of other causes (predominantly cerebral trauma but
also asphyxia). As kidneys from paediatric donors are not
fully developed, such kidneys might have lower graft func-
tion, and would thus not follow the well-established linear
relationship between increasing donor age and decreasing
graft function. Such donors were thus excluded from the
analysis. Please see Data Characteristics in the Supplemen-
tary Materials for a full description of the cohorts.

All seven cohorts had genotype array data available which
was subject to standard quality control for minor allele fre-
quency, genotyping rate and missingness. All individuals
were selected to be unrelated to the level of 3rd degree using
KING [18] software, as well as of European ancestry, identi-
fied using principal components of ancestry. See Data QC
and Procession section of Supplementary Materials for fur-
ther details.

Supplementary Table 1 provides similar cohort charac-
teristics broken down by donor type (living vs deceased).
eGFR was calculated using the CKD-EPI Creatinine Equa-
tion (2021) [19]. Individuals (n=2870) from the Peoples of
the British Isles dataset were used as controls for all cohorts
[20].

Polygenic risk score calculation

We calculated donor polygenic risk score for stroke, intracranial aneurysm and hypertension using published genome-wide association studies of European ancestry for each trait [7-9]. Further details of these genome-wide association studies can be found in Supplementary Table S2. Polygenic risk scores were calculated using PRSice2 [21], selecting alleles with a *p*-value threshold greater than 0.5, physical distance threshold for clumping of 250 kb and linkage
disequilibrium threshold ($r^2$) of 0.1. Analysis was conducted in R, using version 4.2.1 (2022-06-23) [22].

## Comparison of polygenic burden between groups

We used a Kruskal-Wallis test, and a Dunn test to investigate differences in polygenic burden between our 4 groups of individuals (healthy controls, living donors, deceased donors who died of stroke, and deceased donors who died of other causes). We conducted these tests separately for each of the three polygenic risk scores. The Dunn test accounts for multiple testing.

## Influence of donor polygenic burden on age of death in donors who died of stroke

We created meta-regressions using the R package *meta* to investigate the role of polygenic burden for each trait on age of death in donors who died of stroke. Separate models were constructed for each polygenic risk score, taking donor sex and the first four principal components of genetic ancestry as covariates. All the assumptions of a linear model were checked (residuals vs fitted, normal Q-Q, scale-location, and residuals vs leverage). The variance explained by each polygenic risk score was calculated along with the heterogeneity of each model.

Donors who died of stroke were split into 3 groups for each polygenic risk score, defined in the following way: high risk (Top 10% of risk for each polygenic risk score), intermediate risk (Middle 80% of risk), and low risk (Bottom 10% of risk). This was done separately for each polygenic risk score. Median donor age of death was compared between each of these groups.

## Impact of donor polygenic risk score on graft survival

All donors were split into the same 3 groups of risk (high, intermediate and low, as previously defined) for each trait. Median graft survival was compared between each of these groups.

We created meta-regressions using the R package *meta* to investigate the role of polygenic burden for each trait on graft survival. Separate Cox proportional hazards models were constructed for each polygenic risk score, each taking donor and recipient sex, donor and recipient age, year of transplant, and whether it was the recipient's first transplant or not, and the first four principal components of genetic ancestry as covariates. Donor age initially failed the proportional hazards assumption, so was then stratified into 2 categories (<50 years, ≥ 50 years). The variance explained by each polygenic risk score was calculated along with the heterogeneity of each model. Additionally, this analysis was

split by donor type (stroke vs other cause of death (COD)) to investigate if there was a difference in the impact of polygenic risk score on graft survival between the two groups.

## Impact of donor polygenic risk score on graft function

Recipient graft function was defined as eGFR at 1- and 5-years post-transplant. We created 3 meta-regression models (one for each polygenic risk score) using the R package *meta* to predict recipient eGFR at 1-year post-transplant. These models all included the following covariates: donor sex, donor age, donor type, recipient age, recipient sex, year of transplant, and first transplant. All the standard assumptions of linear models were tested. The variance explained by each polygenic risk score was calculated along with the heterogeneity of each model. A similar process was carried out to predict eGFR at 5 years.

We also created a similar model to predict eGFR at 5 years. We tested for the assumptions of non-linearity, homogeneity of variance, influential observations, collinearity, normality of residuals, and normality of random effects (Supplementary Figures S4, S5). The heterogeneity of the models was also tested using the *rma* function from the *metafor* R package.

Again, we split all the individuals into the same 3 groups of risk (high, intermediate and low, as previously defined) for each trait, and compared eGFR at 1 and 5 years between these groups, as well as graft survival.

## Results

Data from 6666 individuals passed QC and were included in the analysis. Clinical characteristics of the dataset are shown in Table 1. The mean donor age was 49 years, with more male donors (53%) than female. Among the donors, 1582 (24%) were living, 3113 (47%) died of stroke and, 1971 (30%) died of other causes of death. Those who died of stroke were the oldest (54 years old), followed by living donors (45 years old) and donors who died of other causes (44 years old) (see Supplementary Table S1).

The data suggest a significant difference in recipient graft survival times depending on the donor type. Organs from living donors have the longest (death-censored) graft survival, followed by those who died from other causes, with organs from deceased donors who died of stroke having the worst graft survival (*p* < 0.0001, see Supplementary Figure S1). Grafts from younger donors (<45 years) also have better eGFR at 1-year post-transplant (see Supplementary Figure S2).

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<table><caption>**Table 1** Summary statistics for the meta-analysis split into each cohort</caption><thead><tr><th>Variable</th><th>Overall</th><th>DeKAF</th><th>FRCBS</th><th>GEN03</th><th>KiT-GENIE</th><th>QUB</th><th>TL</th><th>UKIRTC</th></tr></thead><tbody><tr><td>Number of patients</td><td>6,666</td><td>684</td><td>929</td><td>476</td><td>1853</td><td>133</td><td>696</td><td>1895</td></tr><tr><td>Donor age, median (min-max)</td><td>50 (18–90)</td><td>44 (18–70)</td><td>58 (18–77)</td><td>45 (18–71)</td><td>55 (18–90)</td><td>40 (18–68)</td><td>46 (18–72)</td><td>47 (18–81)</td></tr><tr><td>Female donor, n (%)</td><td>3106 (47)</td><td>405 (59)</td><td>433 (47)</td><td>273 (57)</td><td>776 (42)</td><td>58 (44)</td><td>344 (49)</td><td>817 (43)</td></tr><tr><td>Donor type</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Living, n (%)</td><td>1582 (24)</td><td>684 (100)</td><td>0 (0)</td><td>476 (100)</td><td>319 (17)</td><td>0 (0)</td><td>103 (15)</td><td>0 (0)</td></tr><tr><td>Other COD, n (%)</td><td>1971 (30)</td><td>0 (0)</td><td>311 (33)</td><td>0 (0%)</td><td>655 (35)</td><td>55 (41)</td><td>249 (36)</td><td>701 (37)</td></tr><tr><td>Stroke COD, n (%)</td><td>3113 (47)</td><td>0 (0)</td><td>618 (67)</td><td>0 (0)</td><td>879 (47)</td><td>78 (59)</td><td>344 (49)</td><td>1194 (63)</td></tr><tr><td>Recipient age, median (min-max)</td><td>51 (0–84)</td><td>51 (0–83)</td><td>57 (18–79)</td><td>51 (1–81)</td><td>53 (18–84)</td><td>43 (4–72)</td><td>49 (15–74)</td><td>47 (18–79)</td></tr><tr><td>Female recipient, n (%)</td><td>2391 (36)</td><td>231 (34)</td><td>290 (31)</td><td>177 (37)</td><td>657 (35)</td><td>56 (42)</td><td>282 (41)</td><td>698 (37)</td></tr><tr><td>Year of transplant, median (min-max)</td><td>2007 (1981–2020)</td><td>2008 (2006–2010)</td><td>2014 (2007–2017)</td><td>2014 (2012–2015)</td><td>2011 (1999–2020)</td><td>1997 (1986–2005)</td><td>2000 (1993–2008)</td><td>2001 (1981–2007)</td></tr><tr><td>PRD</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Glomerulonephritis</td><td>772 (12)</td><td>175 (26)</td><td>125 (13)</td><td>162 (34)</td><td>0 (0)</td><td>9 (7)</td><td>139 (20)</td><td>162 (9)</td></tr><tr><td>IgA nephropathy</td><td>313 (5)</td><td>0 (0)</td><td>119 (13)</td><td>0 (0)</td><td>0 (0)</td><td>10 (8)</td><td>49 (7)</td><td>135 (7)</td></tr><tr><td>Other</td><td>3283 (49)</td><td>198 (29)</td><td>293 (32)</td><td>122 (26)</td><td>1569 (85)</td><td>74 (56)</td><td>311 (45)</td><td>716 (38)</td></tr><tr><td>PKD</td><td>990 (15)</td><td>107 (16)</td><td>191 (21)</td><td>76 (16)</td><td>284 (15)</td><td>20 (15)</td><td>89 (13)</td><td>223 (12)</td></tr><tr><td>Type 2 diabetes</td><td>366 (6)</td><td>175 (26)</td><td>76 (8)</td><td>86 (18)</td><td>0 (0)</td><td>4 (3.)</td><td>5 (1)</td><td>20 (1)</td></tr><tr><td>Unknown</td><td>942 (14)</td><td>29 (4.2)</td><td>125 (13)</td><td>30 (6)</td><td>0 (0)</td><td>16 (12)</td><td>103 (15)</td><td>639 (34)</td></tr><tr><td>First transplant, n (%)</td><td>5852 (88)</td><td>603 (88)</td><td>929 (100)</td><td>420 (88)</td><td>1450 (78)</td><td>133 (100)</td><td>637 (92)</td><td>1680 (89)</td></tr><tr><td>Follow up, median (min-max)</td><td>5 (0–25)</td><td>2 (0.4–5)</td><td>3 (0–10)</td><td>2 (0.4–3)</td><td>6 (0–21)</td><td>8 (0–25)</td><td>7 (0–17)</td><td>8 (0–25)</td></tr><tr><td>Graft status, n (%)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Censored</td><td>5577 (84)</td><td>671 (98)</td><td>871 (94)</td><td>474 (99.6)</td><td>1439 (78)</td><td>92 (69)</td><td>586 (84)</td><td>1444 (76)</td></tr><tr><td>Rejected</td><td>1089 (16)</td><td>13 (2)</td><td>58 (6)</td><td>2 (0.4)</td><td>414 (22)</td><td>41 (31)</td><td>110 (16)</td><td>451 (24)</td></tr><tr><td>eGFR at 1 year, median (min-max)</td><td>53 (4–185)</td><td>60 (4–178)</td><td>54 (6–135)</td><td>62 (16–185)</td><td>51 (8–129)</td><td>NA</td><td>47 (4–124)</td><td>50 (6–124)</td></tr><tr><td>Unknown, n (%)</td><td>1030 (15)</td><td>0 (0)</td><td>269 (29)</td><td>0 (0)</td><td>168 (9)</td><td>133 (100)</td><td>59 (8)</td><td>401 (21)</td></tr><tr><td>eGFR at 5 years, median (min-max)</td><td>49 (3–124)</td><td>NA</td><td>50 (13–106)</td><td>NA</td><td>50 (7–122)</td><td>NA</td><td>50 (3–124)</td><td>48 (6–121)</td></tr><tr><td>Unknown</td><td>3865</td><td>684</td><td>829</td><td>476</td><td>839</td><td>133</td><td>215</td><td>689</td></tr></tbody></table>

Descriptive characteristics for the following cohorts: eGFR is in units of mL/min/1.73 m². First transplant refers to whether it is the recipient's first transplant or not. The numbers in each cohort refer to the number of donor kidneys rather than just the number of donors, as one deceased donor may donate two kidneys. Follow up is the mean death-censored graft survival time. Further information on each cohort can be found in Supplementary Materials description of datasets

DeKAF Deterioration of kidney allograft function, FRCBS Finnish red cross blood service, KiT-GENIE kidney transplantation-genomic investigation of essential clinical concerns, TL transplant lines, QUB Queen's University Belfast, UKIRTC United Kingdom and Ireland Renal Transplant consortium. PRD primary renal disease at transplant, COD cause of death, PKD polycystic kidney disease
## Comparison of polygenic burden for stroke, intracranial aneurysm, and hypertension across donors and controls

We first tested the hypothesis that deceased donors have elevated genetic burden for the traits of intracranial aneurysm, stroke, and hypertension compared to both living donors and healthy controls. The donors deceased from stroke had significantly higher polygenic burden than the living donors (Bonferroni adjusted *p* values of 3e-3, 7e-12, 1e-7 for hypertension, intracranial aneurysm, and stroke, respectively) (Fig. 1). As might be expected, healthy controls and living donors had a similar polygenic burden with no statistically significant difference for any of our traits of interest.

## Influence of polygenic burden for hypertension, intracranial aneurysm, and stroke on donor age of death

Next, we investigated if donor polygenic burden for hypertension, intracranial aneurysm, and stroke had an impact on donor age of death among the donors who died of stroke.

We observed that a 1-standard deviation (SD) increase in intracranial aneurysm polygenic risk score results in a 0.57 year (~7 month) decrease in the age of death (95% CI 1.04–0.11 years, Supplementary figure S3).

## Donor polygenic burden and impact on graft outcome and function

Discretizing polygenic burden into 3 groups, (bottom 10%, middle 80% and top 10%, see Methods), we found that those with the highest hypertension polygenic burden were 44% more likely to develop graft failure compared to those with the lowest polygenic burden (95% CI 7%–93%) (Fig. 2A). We found no significant effect of intracranial aneurysm and stroke polygenic risk score on graft survival (Fig. 2A).

To investigate whether donor polygenic burden had an impact on recipient graft function we created a multivariate mixed effects linear model to predict eGFR at 1-year post-transplant based on donor polygenic risk score. We found that both hypertension and intracranial aneurysm polygenic risk scores were significant predictors of graft function, with estimated effect sizes of –1.24 (95% CI

**Fig. 1** Cerebrovascular polygenic burden across donor type and controls. Mean value for each cerebrovascular polygenic risk score is given (along with 95% CI) for each donor type and the healthy controls. Bonferroni-adjusted *p*-value significance levels comparing the mean values in each group are above the error bars. Living donors have similar levels of polygenic burden to healthy controls. Deceased donors who died of stroke have elevated polygenic burden for each trait compared to healthy controls. Deceased donors who died of other causes appear to have slightly higher polygenic burden for each trait compared to healthy controls, but still lower than donors who died of stroke

<figure><img src="image_15.png" /></figure>

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A

<figure><img src="image_17.png" /></figure>

**Fig. 2** The influence of donor polygenic risk score on recipient graft survival. A Cox proportional hazards models for recipient graft survival (one model for each PRS: hypertension, IA, and stroke). Donor sex, donor age, recipient sex, recipient age, whether it was the recipient's first transplant, year of transplant, and first 4 principal components of genetic ancestry are taken as covariates in each model. An individual who received a donor kidney in the top decile of polygenic risk for hypertension is 44% more likely to develop

B

<figure><img src="image_18.png" /></figure>

graft failure than those in the bottom decile of polygenic risk (95% CI 7%–93%). B Kaplan–Meier plot for graft survival split by donor hypertension polygenic risk (high vs low). *p*-value for differences in survival=0.05. Finnish Red Cross Blood Service (FRCBS), kidney transplantation-genomic investigation of essential clinical concerns (KiT-GENIE), transplant lines (TL), United Kingdom and Ireland Renal Transplant consortium (UKIRTC)

– 1.73 to – 0.76) and – 0.70 (95% CI – 1.16 to – 0.24) (Fig. 3A). A one standard deviation increase in hypertension polygenic risk score thus resulted in a 1.24 mL/min/1.73 m² decrease in eGFR at 1-year post-transplant.

We found significant differences in recipient eGFR at 1-year post-transplant between the individuals with low, intermediate and high polygenic burden for the traits of intracranial aneurysm and hypertension (Fig. 3B). Of interest, we found that the value for eGFR in the individuals with high burden for hypertension was 52.5 mL/min/1.73 m², whereas in the low burden individuals it was 57.1 mL/min/1.73 m² (Bonferroni adjusted *p*-value: 0.002). The corresponding values for intracranial aneurysm were 52.0 mL/min/1.73 m² and 56.0 mL/min/1.73 m² (Bonferroni adjusted *p*-value: 0.003).

We analysed the impact of extremes of polygenic risk score for intracranial aneurysm, hypertension and stroke on eGFR at 1 year. Comparing different definitions of high and low polygenic risk score we have shown the most marked effect on eGFR when individuals in the top 10% for polygenic risk score for hypertension were compared with those in the bottom 10%. These groups showed a difference of 4.3 mL/min/1.73 m² in GFR at

1-year post-transplant. Similar observations were made for other groups (Supplementary Table S3).

Similarly, we created a multivariate mixed effects linear model to predict recipient eGFR at 5-years post-transplant based on donor polygenic risk score for hypertension, stroke, and intracranial aneurysm but did not find any impact at 5 years, possibly due to a lack of power, resulting from much more missing data in relation to eGFR at 5 years post-transplant.

Discussion

Here we investigated the impact of donor polygenic burden for cerebrovascular traits on donor age of death and recipient graft outcomes, in 6666 European ancestry kidney transplants. We found that kidney donors who died of stroke had elevated intracranial aneurysm and hypertension polygenic risk scores compared to healthy controls and living donors. The same two polygenic risk scores also had a significant impact on donor age of death and recipient graft function. To our knowledge, this is the first time that polygenic burden for
A

<figure><img src="image_19.png" /></figure>

**Fig. 3** The influence of donor polygenic risk score on recipient eGFR at 1-year post-transplant. A Linear models for recipient eGFR at 1-year post-transplant (one model for each PRS: hypertension, IA, and stroke). Donor sex, donor age, recipient sex, recipient age, whether it was the recipient's first transplant, year of transplant, and first 4 principal components of genetic ancestry are included as covariates in each model. A standard deviation increase in donor IA and hypertension polygenic risk score results in 0.7 and 1.24 mL/

<figure><img src="image_20.png" /></figure>

min/1.73 m² decrease in recipient eGFR at 1-year post-transplant, respectively. eGFR was calculated using creatinine-based CKD-EPI equation. B Donor polygenic risk was split into low (bottom 10% of risk), intermediate (middle 80%) and high (top 10%). p-values are adjusted for multiple testing using a Bonferroni correction. Donor kidneys with low polygenic burden for hypertension and IA result in an approximately 5 mL/min/1.73 m² higher eGFR at 1 year post-transplant

traits related to cerebrovascular disease have been explored
in transplant donors.

We have observed a significant effect of high polygenic risk score for hypertension having reduced graft survival when compared to those with low polygenic risk score for hypertension. This is also associated with significant association with eGFR at 1 year with patients in the highest decile for hypertension polygenic risk score having a 4.6 mL/min/1.73 m² lower eGFR than those with the lowest decile. Similarly, the results for intracranial aneurysm demonstrated a 4 mL/min/1.73 m² difference between the highest and lowest decile. The observed association between donor polygenic risk score for hypertension and eGFR is notable considering the recent demonstration of lower eGFR in kidneys from hypertensive living donors [23].

This work has several limitations. The predictive ability
of any polygenic risk score depends on the power of the
genome-wide association studies used to generate it. While
we leveraged the largest intracranial aneurysm, stroke and
hypertension genome-wide association studies available,
future genome-wide association studies for these traits will
only increase in size, enabling more powerful polygenic risk
scores. Focused genome-wide association studies for suba-
rachnoid haemorrhage resulting in the death of the patient

would enable more targeted donor polygenic risk scores for
use in the transplant setting.

Just over 15% (n = 1047) of transplants in our study date
from before the millennium, providing both advantages and
disadvantages. Whilst we have decades of follow-up for
some transplants, outcomes have improved significantly as
the field has evolved. We have attempted to control for this
by including the year of transplant as a covariate. However,
our analysis also lacked some known predictors of transplant
outcome, including the kidney donor profile index (KDPI)
and expanded criteria donor (ECD). As most of the trans-
plants predated the introduction of the kidney donor profile
index and expanded criteria donor, we were unable to con-
trol for these. As a result, it was impossible to compare the
features used in the clinic to guide donor eligibility. How-
ever, donor sex was included as a covariate, as were donor
age and donor cause of death, two of the most important
criteria in calculating the kidney donor profile index. Data
points for eGFR at 5 years post-transplant were also unavail-
able in 58% of the cohort.

Immunological mechanisms are considered a major con-
tributor to graft loss. Our results are independent of immu-
nological mechanisms and demonstrate that donor genetic
mechanisms associated with hypertension and arterial

<figure><img src="image_21.png" /></figure>
disease influence long-term graft function. These observa-
tions may explain some of the mechanisms associated with
worse long-term function from deceased donors, particularly
intracranial haemorrhage donors, compared to live donors,
and this strategy may be applicable to all types of trans-
planted organs.

With genome-wide association studies becoming increas-
ingly powerful and more predictive, in the future polygenic
risk scores may be able to explain more of the heterogeneity
in graft survival and function post-transplant, and thus play
a role in transplant allocation decisions. Those who receive
a kidney with a high polygenic risk score for hypertension,
for instance, may need more aggressive post-transplant
monitoring.

Donors who die of cerebrovascular disease likely have
a generalized pan-vascular disease process [24] which is
reflected in the increased polygenic risk score for hyper-
tension. These polygenic risk score signals may represent
diverse physiological mechanisms that likely affect the
transplanted kidney and contribute to the reduced graft func-
tion in kidneys that come from donors with high polygenic
burden. As polygenic scores become more powerful, donor
polygenic risk scores could potentially be included in trans-
plant allocation decisions in the future, but more research is
required in this area, particularly in terms of the interaction
between donor and recipient polygenic risk scores.

Collectively, the results presented here demonstrate the
impact of inherited factors associated with donors' death on
long-term graft survival and function. Collaborative efforts
on cohorts with long and detailed clinical follow-up includ-
ing all the variables utilized within the kidney donor profile
index system are required to validate these findings before
they can have a role in future organ allocation policies.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s40620-024-01973-0.

**Acknowledgements** This study makes use of transplant data from a number of transplant centers, all described in the methods. We thank all of our collaborators for access to their data.

**Author contributions** The authors confirm contribution to the paper as follows: study conception and design: KEC, EG, PJC, GLC; data collection: KEC, EG, VM, KAB, EAEE, COS, CH, AJMK, APM, PJM, MHB, WG, PAJ, AKI, BJK, GML, SM, IH, KH, JP, HY, PCH, JS, SS, PAK, SL; analysis and interpretation of results: KEC, EG, GLC, PJC, SM; draft manuscript preparation: KEC, EG, GLC, PJC. All authors reviewed the results and approved the final version of the manuscript.

**Funding** Open Access funding provided by the IReL Consortium. This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant number 18/ CRT/6214. This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113 and 085475. The KiT-GENIE cohort has been funded by several entities: (1) The Etoiles Montantes funding by the

Pays de la Loire region (n°2018-09998), (2) The IRCT Dialyse research project by the Société Francophone de Néphrologie, Dialyse et Transplantation (SFNDT), (3) The Greffe research project by French Agence de la Biomédecine (ABM, n°18GREFFE014). In addition, we thank the Roche Pharma, Novartis, Astellas, Chiesi, Sandoz and Sanofi laboratories for supporting the DIVAT cohort as the CENTAURE Foundation (http://www.fondation-centaure.org). The QUB contribution was supported by the HSC R&D division (STL/5569/19), UKRI (MRC MCPC_20026), and Science Foundation Ireland—NI Department for the Economy Investigator Program Partnership Award (15/IA/3152). The UKIRTC was supported by grants awarded from the Wellcome Trust (090355/A/09/Z, 090355/B/09/Z and 088849/Z/09/Z, “WTCCC3”), the Medical Research Council (grants G0600698 and MR/J006742/1 to S.H.S.; G0802068 to G.M.L. and MR/K002996/1 to G.M.L.; grants G0801537/ID: 88245), Guy’s and St Thomas’ Charity (grants R080530 and R090782) to M.H.F. and G.M.L., from the European Union FP7 (grant agreement no HEALTHF5-2010-260687 to M.H.F. and project number 305147: BIO-DrIM to M.H.F. and I.R.M.); and by the National Institute for Health Research Biomedical Research Centre at Guy’s and St Thomas’ and King’s College London. FRCBS cohort study has been funded by the Government of Finland VTR funding, Munuaissäätö (to SM), Suomen Transplantaatiokirurginen yhdistys ry (to SM), and Academy of Finland (to JP). The genotype imputation reference panel used for the research were obtained from THL Biobank (study number: BB2019_12). We thank all study participants for their generous participation in biobank research. EAEE reports funds by the Royal College of Surgeons in Ireland Blackrock Clinic STAR PhD. NICOLA was used as a reference to generate the PRSs; NICOLA’s molecular data was supported by The Atlantic Philanthropies and the Economic and Social Research Council (ES/L008459/1).

**Data availability** The data used in this study are not publicly avail-
able due to concerns regarding patient confidentiality and privacy.
Access to the data can be requested from each participating center
individually, subject to their respective data sharing policies and ethi-
cal considerations.

Declarations

**Conflict of interest** Unless otherwise stated all authors have no con-
flicts of interest. Ilkka Helanterä reports receiving research funding
from MSD and Hansa Biopharma, and has ongoing consultancy agree-
ments with Novartis, Hansa Biopharma, and Takeda.

**Ethical approval** This study, which involved human participants, their data or biological material, was approved (or granted exemption) by the appropriate institutional and/or national research ethics committee which was the Hammersmith and Queen Charlotte’s & Chelsea Research Ethics Committee REC No 08/H0707/1.

**Human and animal rights** The study including human participants has been performed in accordance with the ethical standards of the Declaration of Helsinki and its later amendments.

**Informed consent** All participants have provided their informed con-
sent to participate and publish this research.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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