The use of metabolomics and machine learning algorithms to predict post-transplant diabetes mellitus in renal transplant patients on Tacrolimus therapy.

Abstract

Background and aim: Tacrolimus (TAC) has significantly improved kidney graft survival following transplantation, though it is associated with adverse side effects. The most prevalent complication resulting from excessive TAC exposure is the onset of de novo diabetes mellitus (DM), a condition that can negatively impact both renal graft function and patient outcomes. De novo DM is linked to an increased risk of chronic transplant dysfunction, as well as cardiovascular morbidity and mortality. Although the underlying mechanisms remain unclear, emerging research in the field of omics shows promise. The aim of this study was to investigate the metabolomic profile of kidney transplant patients who developed de novo DM, in comparison to those who did not, following TAC exposure, using untargeted metabolomic analysis through ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) and machine learning algorithms.

Methods: A cohort of 34 kidney transplant patients on a Tacrolimus regimen for at least 6 months was enrolled in the study, with serum samples collected from each patient. Comprehensive profiling of serum metabolites was performed, enabling the classification of patients into de novo diabetes mellitus and non diabetes groups. The metabolomic analysis of serum was conducted using UHPLC-MS.

Results: Of the 34 patients, 16 were diagnosed with TAC-induced diabetes. A total of 334 metabolites were identified in the serum samples, of which 10 demonstrated a significant correlation with the de novo diabetes mellitus group. Most of these metabolites were linked to alterations in lipid metabolism.

Conclusion: 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.