Quality of Life among End-stage Renal Disease Treatments and Economic Evaluation of Renal Transplantation and Hemodialysis Treatments

Molecular Markers of Kidney Transplantation Outcome: Current Omics Tools and Future Developments

Purpose of review: The emerging field of molecular predictive medicine is aiming to change the traditional medical approach in renal transplantation. Many studies have explored potential biomarker molecules with predictive properties in renal transplantation, issued from omics research. Herein, we review the biomarker molecules of four technologies (i.e., Genomics, Transcriptomics, Proteomics, and Metabolomics) associated with favorable kidney transplant outcomes. Recent findings: Several panels of molecules have been associated with the outcome that the majority of markers are related to inflammation and immune response; although. other molecular ontologies are also represented, such as proteasome, growth, regeneration, and drug metabolism. Throughout this review, we highlight the lack of properly validated statistical demonstration. Indeed, the most preeminent molecular panels either remain at the limited size study stage or are not confirmed during large-scale studies. At the core of this problem, we identify the methodological shortcomings and propose a comprehensive workflow for discovery and validation of molecular biomarkers that aims to improve the relevance of these tools in the future. Summary: Overall, adopting a patient management through omics approach could bring remarkable improvement to transplantation success. An increased effort and investment between scientists, medical biologists, and clinicians seem to be the path toward a proper solution.

Tacrolimus trough levels in kidney transplant recipients

BACKGROUND

It is very important that kidney transplant recipients (KTRs) take immunosuppressive drugs to prevent graft rejection. This study aimed to identify the tacrolimus trough levels (TTL)-mean, TTL-standard deviation (SD), and TTL- coefficient of variation (CV) as well as factors affecting these values over a 2-year period in clinically stable patients > 5 years after kidney transplantation (KT).

METHODS

This retrospective study analyzed data from 248 adult outpatients > 5 years after KT. Medical chart data, including TTL, graft rejection, and tacrolimus dose change during a 2-year period, between January 2017 and December 2018, were collected. Multivariable regression analyses were conducted to determine the factors influencing the TTL-mean, TTL-SD, and TTL-CV.

RESULTS

The TTL-mean, TTL-SD, and TTL-CV were 6.00 ± 1.07 ng/mL, 1.51 ± 1.09 ng/mL, and 0.25 ± 0.14, respectively. The TTL-mean, TTL-SD, and TTL-CV did not differ according to sex, type of donor, retransplant, pretransplant kidney disease, body mass index, or posttransplant time; hence, they are stable in kidney transplant recipients > 5 years after KT. The higher the TTL-mean, the higher the TTL-SD. Age and the TTL-SD significantly predicted the TTL-mean (p < .001). Tacrolimus dose change and the TTL-mean significantly predicted the TTL-SD (p < .001). Tacrolimus dose change significantly predicted the TTL-CV (p = .008).

CONCLUSION

In clinically stable KTRs, TTL-SD and TTL-CV change sensitively in relation to tacrolimus dose changes. Therefore, changes in TTL-SD and TTL-CV in stable KTRs with no tacrolimus dose change require medical interest and attention.