The impact of donor and recipient common clinical and genetic variation on estimated glomerular filtration rate in a European renal transplant population

Genetic variation across the HLA is known to influence renal‐transplant outcome. However, the impact of genetic variation beyond the HLA is less clear. We tested the association of common genetic variation and clinical characteristics, from both the donor and recipient, with post‐transplant eGFR at different time‐points, out to 5‐years post‐transplantation. We conducted GWAS meta‐analyses across 10,844 donors and recipients from five European ancestry cohorts. We also analysed the impact of polygenic risk scores (PRS), calculated using genetic variants associated with non‐transplant eGFR, on post‐transplant eGFR. PRS calculated using the recipient genotype alone, as well as combined donor and recipient genotypes were significantly associated with eGFR at 1‐year post‐transplant. 32% of the variability in eGFR at 1‐year post‐transplant was explained by our model containing clinical covariates (including weights for death/graft‐failure), principal components and combined donor‐recipient PRS, with 0.3% contributed by the PRS. No individual genetic variant was significantly associated with eGFR post‐transplant in the GWAS. This is the first study to examine PRS, composed of variants that impact kidney function in the general population, in a post‐transplant context. Despite PRS being a significant predictor of eGFR post‐transplant, the effect size of common genetic factors is limited compared to clinical variables

Predicting Outcomes on the Liver Transplant Waiting List in the United States

BackgroundThe probability of liver transplant and death on the waiting list in the United States varies greatly by donation service area (DSA) due to geographic differences in availability of organs and allocation of priority points, making it difficult for providers to predict likely outcomes after listing. We aimed to develop an online calculator to report outcomes by region and patient characteristics.MethodsUsing the Scientific Registry of Transplant Recipients database, we included all prevalent US adults aged 18 years or older waitlisted for liver transplant, examined on 24 days at least 30 days apart over a 2-year period. Outcomes were determined at intervals of 30 to 365 days. Outcomes are reported by transplant program, DSA, region, and the nation for comparison, and can be shown by allocation or by laboratory model for end-stage liver disease (MELD) score (6-14, 15-24, 25-29, 30-34, 35-40), age, and blood type.ResultsOutcomes varied greatly by DSA; for candidates with allocation MELD 25-29, the 25th and 75th percentiles of liver transplant probability were 30% and 67%, respectively, at 90 days. Corresponding percentiles for death or becoming too sick to undergo transplant were 5% and 9%. Outcomes also varied greatly for candidates with and without MELD exception points.ConclusionsThe waitlist outcome calculator highlights ongoing disparities in access to liver transplant and may assist providers in understanding and counseling their patients about likely outcomes on the waiting list

Delayed hepatocellular carcinoma model for end-stage liver disease exception score improves disparity in access to liver transplant in the United States

UnlabelledThe current system granting liver transplant candidates with hepatocellular carcinoma (HCC) additional Model for End-Stage Liver Disease (MELD) points is controversial due to geographic disparity and uncertainty regarding optimal prioritization of candidates. The current national policy assigns a MELD exception score of 22 immediately upon listing of eligible patients with HCC. The aim of this study was to evaluate the potential effects of delays in granting these exception points on transplant rates for HCC and non-HCC patients. We used Scientific Registry of Transplant Recipients data and liver simulated allocation modeling software and modeled (1) a 3-month delay before granting a MELD exception score of 25, (2) a 6-month delay before granting a score of 28, and (3) a 9-month delay before granting a score of 29. Of all candidates waitlisted between January 1 and December 31, 2010 (n = 28,053), 2773 (9.9%) had an HCC MELD exception. For HCC candidates, transplant rates would be 108.7, 65.0, 44.2, and 33.6 per 100 person-years for the current policy and for 3-, 6-, and 9-month delays, respectively. Corresponding rates would be 30.1, 32.5, 33.9, and 34.8 for non-HCC candidates.ConclusionA delay of 6-9 months would eliminate the geographic variability in the discrepancy between HCC and non-HCC transplant rates under current policy and may allow for more equal access to transplant for all candidates

Expression of representative genes over time in kidney transplant recipients.

<p>Representative time series of fold expression changes relative to baseline (time 0) of some top genes with higher and lower level compared to baseline. Each line on each graph represents the expression of the particular gene in a separate kidney allograft recipient. Note that all patients do not have data all the time points. CD3E = CD3 Epsilon TCR complex; CD3D = CD3 Delta TCR complex; MMP8 = Matrix Metallopeptidase 8; IL7R = Interleukin 7 Receptor; OLFM4 = Olfactomedin 4; KLRC3 = Killer Cell Lectin-like Receptor subfamily C, member 3.</p

Alignment of paired end RNAseq reads with Reference Genome, n = 96.

<p>RNA sequencing reads were aligned to human genome (GRCh37/hg19 assembly) via Tophat2.</p><p>Alignment of paired end RNAseq reads with Reference Genome, n = 96.</p

Top altered pathways from blood following kidney transplant.

<p>Top canonical gene pathways altered at week 1, months 3 and 6 compared to baseline using Ingenuity Pathway Analysis of DEGs with false discovery rate (FDR) < 0.01 and 2 or greater fold change. The number of DEGs in the pathway is shown in parentheses.</p><p>DEG = Differentially Expressed Gene.</p><p>Baseline = prior to transplant.</p><p>Top altered pathways from blood following kidney transplant.</p