Role of pharmacogenomics in dialysis and transplantation.
ABSTRACT: Pharmacogenomics is the study of differences in drug response on the basis of individual genetic background. With rapidly advancing genomic technologies and decreased costs of genotyping, the field of pharmacogenomics continues to develop. Application to patients with kidney disease provides growing opportunities for improving drug therapy.Pharmacogenomics studies are lacking in patients with chronic kidney disease and dialysis, but are abundant in the kidney transplant field. A potentially clinically actionable genetic variant exists in the CYP3A5 gene, with the initial tacrolimus dose selection being optimized based on CYP3A5 genotype. Although many pharmacogenomics studies have focused on transplant immunosuppression pharmacokinetics, an expanding literature on pharmacodynamic outcomes, such as calcineurin inhibitor toxicity and new onset diabetes, is providing new information on patients at risk.Appropriately powered pharmacogenomics studies with well-defined phenotypes are needed to validate existing studies and unearth new findings in patients with kidney disease, especially the chronic kidney disease and dialysis population.
Project description:Transplant recipients receive potent immunosuppressive drugs in order to prevent graft rejection. Therapeutic drug monitoring is the current approach to guide the dosing of calcineurin inhibitors, mammalian target of rapamycin inhibitors (mTORi) and mofetil mycophenolate. Target concentrations used in pediatric patients are extrapolated from adult studies. Gene polymorphisms in metabolizing enzymes and drug transporters such as cytochromes CYP3A4 and CYP3A5, UDP-glucuronosyl transferase, and P-glycoprotein are known to influence the pharmacokinetics and dose requirements of immunosuppressants. The implications of pharmacogenomics in this patient population is discussed. Genetic information can help with achieving target concentrations in the early post-transplant period, avoiding adverse drug reactions and drug-drug interactions. Evidence about genetic studies and transplant outcomes is revised.
Project description:Pharmacogenomics is a tool for practitioners to provide precision pharmacotherapy using genomics. All providers are likely to encounter genomic data in practice with the expectation that they are able to successfully apply it to patient care. Pharmacogenomics tests for genetic variations in genes that are responsible for drug metabolism, transport, and targets of drug action. Variations can increase the risk for drug toxicity or poor efficacy. Pharmacogenomics can, therefore, be used to help select the best medication or aid in dosing. Nephrologists routinely treat cardiovascular disease and manage patients after kidney transplantation, two situations for which there are several high-evidence clinical recommendations for commonly used anticoagulants, antiplatelets, statins, and transplant medications. Successful use of pharmacogenomics in practice requires that providers are familiar with how to access and use pharmacogenomics resources. Similarly, clinical decision making related to whether to use existing data, whether to order testing, and if data should be used in practice is needed to deliver precision medicine. Pharmacogenomics is applicable to virtually every medical specialty, and nephrologists are well positioned to be implementation leaders.
Project description:OBJECTIVE:Sirolimus has been used to treat paediatric kaposiform haemangioendothelioma patients. However, there is considerable pharmacokinetic variability among individuals, and it is difficult to develop an initial dosing regimen. The goal of the present study is to recommend an initial sirolimus dose in paediatric kaposiform haemangioendothelioma patients based on population pharmacokinetics and pharmacogenomics. METHODS:This was a retrospective clinical study. A population pharmacokinetics model was established and population characteristics, laboratory test results, drug combinations, and pharmacogenomics were considered as potential covariates. The Monte Carlo method was used to simulate the optimal initial dosage. RESULTS:The final covariates that affect sirolimus clearance include weight and the CYP3A5 genotype. The initial dosage of sirolimus for individuals with CYP3A5*3/*3 was 0.20?mg/kg split into two doses for 5 to 60 kg body weight. For individuals with CYP3A5*1, the initial dose was 0.23?mg/kg split into two doses for 5 to 30 kg body weight and 0.20?mg/kg split into two doses for 30 to 60 kg body weight. CONCLUSION:The recommendation for the initial sirolimus dose in paediatric kaposiform haemangioendothelioma patients was based on population pharmacokinetics and pharmacogenomics. This study may provide practical value for sirolimus clinical use in paediatric kaposiform haemangioendothelioma patients.
Project description:Dialysis facilities in the United States are required to educate patients with end-stage renal disease about all treatment options, including kidney transplantation. Patients receiving dialysis typically require a referral for kidney transplant evaluation at a transplant center from a dialysis facility to start the transplantation process, but the proportion of patients referred for transplantation is unknown.To describe variation in dialysis facility-level referral for kidney transplant evaluation and factors associated with referral among patients initiating dialysis in Georgia, the US state with the lowest kidney transplantation rates.Examination of United States Renal Data System data from a cohort of 15,279 incident, adult (18-69 years) patients with end-stage renal disease from 308 Georgia dialysis facilities from January 2005 to September 2011, followed up through September 2012, linked to kidney transplant referral data collected from adult transplant centers in Georgia in the same period.Referral for kidney transplant evaluation within 1 year of starting dialysis at any of the 3 Georgia transplant centers was the primary outcome; placement on the deceased donor waiting list was also examined.The median within-facility percentage of patients referred within 1 year of starting dialysis was 24.4% (interquartile range, 16.7%-33.3%) and varied from 0% to 75.0%. Facilities in the lowest tertile of referral (<19.2%) were more likely to treat patients living in high-poverty neighborhoods (absolute difference, 21.8% [95% CI, 14.1%-29.4%]), had a higher patient to social worker ratio (difference, 22.5 [95% CI, 9.7-35.2]), and were more likely nonprofit (difference, 17.6% [95% CI, 7.7%-27.4%]) compared with facilities in the highest tertile of referral (>31.3%). In multivariable, multilevel analyses, factors associated with lower referral for transplantation, such as older age, white race, and nonprofit facility status, were not always consistent with the factors associated with lower waitlisting.In Georgia overall, a limited proportion of patients treated with dialysis were referred for kidney transplant evaluation between 2005 and 2011, but there was substantial variability in referral among facilities. Variables associated with referral were not always associated with waitlisting, suggesting that different factors may account for disparities in referral.
Project description:Importance:For-profit (vs nonprofit) dialysis facilities have historically had lower kidney transplantation rates, but it is unknown if the pattern holds for living donor and deceased donor kidney transplantation, varies by facility ownership, or has persisted over time in a nationally representative population. Objective:To determine the association between dialysis facility ownership and placement on the deceased donor kidney transplantation waiting list, receipt of a living donor kidney transplant, or receipt of a deceased donor kidney transplant. Design, Setting, and Participants:Retrospective cohort study that included 1?478?564 patients treated at 6511 US dialysis facilities. Adult patients with incident end-stage kidney disease from the US Renal Data System (2000-2016) were linked with facility ownership (Dialysis Facility Compare) and characteristics (Dialysis Facility Report). Exposures:The primary exposure was dialysis facility ownership, which was categorized as nonprofit small chains, nonprofit independent facilities, for-profit large chains (>1000 facilities), for-profit small chains (<1000 facilities), and for-profit independent facilities. Main Outcomes and Measures:Access to kidney transplantation was defined as time from initiation of dialysis to placement on the deceased donor kidney transplantation waiting list, receipt of a living donor kidney transplant, or receipt of a deceased donor kidney transplant. Cumulative incidence differences and multivariable Cox models assessed the association between dialysis facility ownership and each outcome. Results:Among 1?478?564 patients, the median age was 66 years (interquartile range, 55-76 years), with 55.3% male, and 28.1% non-Hispanic black patients. Eighty-seven percent of patients received care at a for-profit dialysis facility. A total of 109?030 patients (7.4%) received care at 435 nonprofit small chain facilities; 78?287 (5.3%) at 324 nonprofit independent facilities; 483?988 (32.7%) at 2239 facilities of large for-profit chain 1; 482?689 (32.6%) at 2082 facilities of large for-profit chain 2; 225?890 (15.3%) at 997 for-profit small chain facilities; and 98?680 (6.7%) at 434 for-profit independent facilities. During the study period, 121?680 patients (8.2%) were placed on the deceased donor waiting list, 23?762 (1.6%) received a living donor kidney transplant, and 49?290 (3.3%) received a deceased donor kidney transplant. For-profit facilities had lower 5-year cumulative incidence differences for each outcome vs nonprofit facilities (deceased donor waiting list: -13.2% [95% CI, -13.4% to -13.0%]; receipt of a living donor kidney transplant: -2.3% [95% CI, -2.4% to -2.3%]; and receipt of a deceased donor kidney transplant: -4.3% [95% CI, -4.4% to -4.2%]). Adjusted Cox analyses showed lower relative rates for each outcome among patients treated at all for-profit vs all nonprofit dialysis facilities: deceased donor waiting list (hazard ratio [HR], 0.36 [95% CI, 0.35 to 0.36]); receipt of a living donor kidney transplant (HR, 0.52 [95% CI, 0.51 to 0.54]); and receipt of a deceased donor kidney transplant (HR, 0.44 [95% CI, 0.44 to 0.45]). Conclusions and Relevance:Among US patients with end-stage kidney disease, receiving dialysis at for-profit facilities compared with nonprofit facilities was associated with a lower likelihood of accessing kidney transplantation. Further research is needed to understand the mechanisms behind this association.
Project description:Background:Many patients with end-stage kidney disease (ESKD) do not appreciate how their survival may differ if treated with a kidney transplant compared with dialysis. A risk calculator (iChoose Kidney) developed and validated in the United States provides individualized mortality estimates for different treatment options (dialysis vs living or deceased donor kidney transplantation). The calculator can be used with patients and families to help patients make more educated treatment decisions. Objective:To validate the iChoose Kidney risk calculator in Ontario, Canada. Design:External validation study. Setting:We used several linked administrative health care databases from Ontario, Canada. Patients:We included 22 520 maintenance dialysis patients and 4505 kidney transplant recipients. Patients entered the cohort between 2004 and 2014. Measurements:Three-year all-cause mortality. Methods:We assessed model discrimination using the C-statistic. We assessed model calibration by comparing the observed versus predicted mortality risk and by using smoothed calibration plots. We used multivariable logistic regression modeling to recalibrate model intercepts using a correction factor, when appropriate. Results:In our final version of the iChoose Kidney model, we included the following variables: age (18-80 years), sex (male, female), race (white, black, other), time on dialysis (<6 months, 6-12 months, >12 months), and patient comorbidities (hypertension, diabetes, and/or cardiovascular disease). Over the 3-year follow-up period, 33.3% of dialysis patients and 6.2% of kidney transplant recipients died. The discriminatory ability was moderate (C-statistic for dialysis: 0.70, 95% confidence interval [CI]: 0.69-0.70, and C-statistic for transplant: 0.72, 95% CI: 0.69-0.75). The 3-year observed and predicted mortality estimates were comparable and even more so after we recalibrated the intercepts in 2 of our models (dialysis and deceased donor kidney transplantation). As done in the United States, we developed a Canadian Web site and an iOS application called Dialysis vs. Kidney Transplant- Estimated Survival in Ontario. Limitations:Missing data in our databases precluded the inclusion of all variables that were in the original iChoose Kidney (ie, patient ethnicity and low albumin). We were unable to perform all preplanned analyses due to the limited sample size. Conclusions:The original iChoose Kidney risk calculator was able to adequately predict mortality in this Canadian (Ontario) cohort of ESKD patients. After minor modifications, the predictive accuracy improved. The Dialysis vs. Kidney Transplant- Estimated Survival in Ontario risk calculator may be a valuable resource to help ESKD patients make an informed decision on pursuing kidney transplantation.
Project description:The Southeastern United States has the lowest kidney transplant rates in the nation, and racial disparities in kidney transplant access are concentrated in this region. The Southeastern Kidney Transplant Coalition (SEKTC) of Georgia, North Carolina, and South Carolina is an academic and community partnership that was formed with the mission to improve access to kidney transplantation and reduce disparities among African American (AA) end stage renal disease (ESRD) patients in the Southeastern United States.We describe the community-based participatory research (CBPR) process utilized in planning the Reducing Disparities In Access to kidNey Transplantation (RaDIANT) Community Study, a trial developed by the SEKTC to reduce health disparities in access to kidney transplantation among AA ESRD patients in Georgia, the state with the lowest kidney transplant rates in the nation. The SEKTC Coalition conducted a needs assessment of the ESRD population in the Southeast and used results to develop a multicomponent, dialysis facility-randomized, quality improvement intervention to improve transplant access among dialysis facilities in GA. A total of 134 dialysis facilities are randomized to receive either: (1) standard of care or "usual" transplant education, or (2) the multicomponent intervention consisting of transplant education and engagement activities targeting dialysis facility leadership, staff, and patients within dialysis facilities. The primary outcome is change in facility-level referral for kidney transplantation from baseline to 12 months; the secondary outcome is reduction in racial disparity in transplant referral.The RaDIANT Community Study aims to improve equity in access to kidney transplantation for ESRD patients in the Southeast.Clinicaltrials.gov number NCT02092727.
Project description:BACKGROUND:Patients on kidney replacement therapy comprise a vulnerable population and may be at increased risk of death from coronavirus disease 2019 (COVID-19). Currently, only limited data are available on outcomes in this patient population. METHODS:We set up the ERACODA (European Renal Association COVID-19 Database) database, which is specifically designed to prospectively collect detailed data on kidney transplant and dialysis patients with COVID-19. For this analysis, patients were included who presented between 1 February and 1 May 2020 and had complete information available on the primary outcome parameter, 28-day mortality. RESULTS:Of the 1073 patients enrolled, 305 (28%) were kidney transplant and 768 (72%) dialysis patients with a mean age of 60?±?13 and 67?±?14?years, respectively. The 28-day probability of death was 21.3% [95% confidence interval (95% CI) 14.3-30.2%] in kidney transplant and 25.0% (95% CI 20.2-30.0%) in dialysis patients. Mortality was primarily associated with advanced age in kidney transplant patients, and with age and frailty in dialysis patients. After adjusting for sex, age and frailty, in-hospital mortality did not significantly differ between transplant and dialysis patients [hazard ratio (HR) 0.81, 95% CI 0.59-1.10, P?=?0.18]. In the subset of dialysis patients who were a candidate for transplantation (n?=?148), 8 patients died within 28?days, as compared with 7 deaths in 23 patients who underwent a kidney transplantation <1 year before presentation (HR adjusted for sex, age and frailty 0.20, 95% CI 0.07-0.56, P?<?0.01). CONCLUSIONS:The 28-day case-fatality rate is high in patients on kidney replacement therapy with COVID-19 and is primarily driven by the risk factors age and frailty. Furthermore, in the first year after kidney transplantation, patients may be at increased risk of COVID-19-related mortality as compared with dialysis patients on the waiting list for transplantation. This information is important in guiding clinical decision-making, and for informing the public and healthcare authorities on the COVID-19-related mortality risk in kidney transplant and dialysis patients.
Project description:BACKGROUND AND OBJECTIVES:Over the past decade, the management of CKD-mineral and bone disorder has changed substantially, altering the pattern of bone disease in CKD. We aimed to evaluate the natural history of kidney bone disease in contemporary kidney transplant recipients and patients on dialysis. DESIGN, SETTINGS, PARTICIPANTS, & MEASUREMENTS:Sixty one patients on dialysis who were referred to kidney transplantation participated in this prospective cohort study during November 2009 and December 2010. We performed baseline bone biopsies while the patients were on dialysis and repeated the procedure in 56 patients at 2 years after kidney transplantation or 2 years after baseline if transplantation was not performed. Measurements of mineral metabolism and bone turnover, as well as dual energy x-ray absorptiometry scans, were obtained concurrently. RESULTS:A total of 37 out of 56 participants received a kidney transplant, of which 27 underwent successful repeat bone biopsy. The proportion of patients with high bone turnover declined from 63% at baseline to 19% at 2 years after kidney transplantation, whereas the proportion of those with low bone turnover increased from 26% to 52%. Of 19 participants remaining on dialysis after 2 years, 13 underwent successful repeat biopsy. The proportion of patients remaining on dialysis with high bone turnover decreased from 69% to 31%, and low bone turnover increased from 8% to 38%. Abnormal bone mineralization increased in transplant recipients from 33% to 44%, but decreased in patients remaining on dialysis from 46% to 15%. Trabecular bone volume showed little change after transplantation, but low bone volume increased in patients remaining on dialysis. Bone mineral density did not correlate with histomorphometric findings. CONCLUSIONS:Bone turnover decreased over time both in patients remaining on dialysis and in kidney transplant recipients. Bone mineral density and bone biomarkers were not associated with bone metabolism changes detected in bone biopsy specimens.
Project description:To develop a dosing equation for tacrolimus, using genetic and clinical factors from a large cohort of kidney transplant recipients. Clinical factors and six genetic variants were screened for importance towards tacrolimus clearance (CL/F).Clinical data, tacrolimus troughs and corresponding doses were collected from 681 kidney transplant recipients in a multicentre observational study in the USA and Canada for the first 6 months post transplant. The patients were genotyped for 2,724 single nucleotide polymorphisms using a customized Affymetrix SNP chip. Clinical factors and the most important SNPs (rs776746, rs12114000, rs3734354, rs4926, rs3135506 and rs2608555) were analysed for their influence on tacrolimus CL/F.The CYP3A5*1 genotype, days post transplant, age, transplant at a steroid sparing centre and calcium channel blocker (CCB) use significantly influenced tacrolimus CL/F. The final model describing CL/F (l h(-1)) was: 38.4 ×[(0.86, if days 6-10) or (0.71, if days 11-180)]×[(1.69, if CYP3A5*1/*3 genotype) or (2.00, if CYP3A5*1/*1 genotype)]× (0.70, if receiving a transplant at a steroid sparing centre) × ([age in years/50](-0.4)) × (0.94, if CCB is present). The dose to achieve the desired trough is then prospectively determined using the individuals CL/F estimate.The CYP3A5*1 genotype and four clinical factors were important for tacrolimus CL/F. An individualized dose is easily determined from the predicted CL/F. This study is important towards individualization of dosing in the clinical setting and may increase the number of patients achieving the target concentration. This equation requires validation in an independent cohort of kidney transplant recipients.