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

Transcriptomic alterations in the heart of non-obese type 2 diabetic Goto-Kakizaki rats

BACKGROUND: There is a spectacular rise in the global prevalence of type 2 diabetes mellitus (T2DM) due to the worldwide obesity epidemic. However, a significant proportion of T2DM patients are non-obese and they also have an increased risk of cardiovascular diseases. As the Goto-Kakizaki (GK) rat is a well-known model of non-obese T2DM, the goal of this study was to investigate the effect of non-obese T2DM on cardiac alterations of the transcriptome in GK rats. METHODS: Fasting blood glucose, serum insulin and cholesterol levels were measured at 7, 11, and 15 weeks of age in male GK and control rats. Oral glucose tolerance test and pancreatic insulin level measurements were performed at 11 weeks of age. At week 15, total RNA was isolated from the myocardium and assayed by rat oligonucleotide microarray for 41,012 genes, and then expression of selected genes was confirmed by qRT-PCR. Gene ontology and protein-protein network analyses were performed to demonstrate potentially characteristic gene alterations and key genes in non-obese T2DM. RESULTS: Fasting blood glucose, serum insulin and cholesterol levels were significantly increased, glucose tolerance and insulin sensitivity were significantly impaired in GK rats as compared to controls. In hearts of GK rats, 204 genes showed significant up-regulation and 303 genes showed down-regulation as compared to controls according to microarray analysis. Genes with significantly altered expression in the heart due to non-obese T2DM includes functional clusters of metabolism (e.g. Cyp2e1, Akr1b10), signal transduction (e.g. Dpp4, Stat3), receptors and ion channels (e.g. Sln, Chrng), membrane and structural proteins (e.g. Tnni1, Mylk2, Col8a1, Adam33), cell growth and differentiation (e.g. Gpc3, Jund), immune response (e.g. C3, C4a), and others (e.g. Lrp8, Msln, Klkc1, Epn3). Gene ontology analysis revealed several significantly enriched functional inter-relationships between genes influenced by non-obese T2DM. Protein-protein interaction analysis demonstrated that Stat is a potential key gene influenced by non-obese T2DM. CONCLUSIONS: Non-obese T2DM alters cardiac gene expression profile. The altered genes may be involved in the development of cardiac pathologies and could be potential therapeutic targets in non-obese T2DM

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