The use of metabolomics and machine learning algorithms to predict post-transplant diabetes mellitus in renal transplant patients on Tacrolimus therapy
The application of metabolomics in kidney transplant patients undergoing a Tacrolimus regimen is both feasible and effective in identifying metabolites associated with de novo diabetes mellitus. This approach may provide valuable insights into the metabolic alterations underlying TAC-induced diabetes.
Authors
- Adriana Munteanu (ORCID: https://orcid.org/0000-0001-6288-6351)
- Oana Antal (ORCID: https://orcid.org/0000-0002-0183-9815)
- Carmen Socaciu (ORCID: https://orcid.org/0000-0002-7352-5057)
- Dan Burghelea (ORCID: https://orcid.org/0000-0002-0570-2875)
- Tudor Moisoiu (ORCID: https://orcid.org/0000-0002-9255-8217)
- Florin Ioan Elec (ORCID: https://orcid.org/0000-0002-0803-2387)
- Ina Maria Kacsó (ORCID: https://orcid.org/0000-0003-1167-9154)
- Alina Elec (ORCID: https://orcid.org/0000-0003-1691-6729)
- Teodor Paul Kacso (ORCID: https://orcid.org/0000-0002-8799-1307)
- Cristina Ivan (ORCID: https://orcid.org/0000-0002-4848-0168)
- Raluca Tabrea
Institutions
- Iuliu Hațieganu University of Medicine and Pharmacy (RO)
- University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca (RO)
- Research Institute for Analytical Instrumentation (RO)
Publication Details
- Journal
- Medicine and Pharmacy Reports
- Published
- 2024-10-24
- DOI
- https://doi.org/10.15386/mpr-2780
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.12