Multiplatform urine metabolomics for non-invasive prediction of one-year renal function decline in kidney transplant recipients: a pilot study

Introduction Kidney transplantation (KTx) provides the best therapeutic outcomes for patients with end-stage renal disease. However, long-term graft survival remains a major clinical challenge, and current biomarkers are insufficient to reliably predict post-transplant kidney function evolution. Identifying early predictors of renal function decline is therefore crucial to improve the monitoring and management of kidney transplant recipients (KTRs). Objectives This pilot study aimed to investigate whether multiplatform urine metabolomics could identify early predictive biomarkers of renal function decline between 3 and 12 months post-KTx. Methods A cohort of 56 French KTRs was recruited. Measured glomerular filtration rate (mGFR) was assessed at 3 (M3) and 12 (M12) months post-transplant, while urine samples were collected at M3. Patients were classified as “progressor” or “stable” based on a ≥ 7% decline or stability in mGFR over the 9-month follow-up period. Untargeted metabolomic profiling was performed on urine samples using complementary Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) platforms. Multivariate statistical analyses were then applied to identify metabolites associated with mGFR evolution. Results Multivariate modeling revealed putative urinary biomarkers associated with renal function trajectories. The strongest predictive performance was achieved using a combined model integrating both MS- and NMR-derived biomarkers, highlighting the complementarity of the two analytical approaches. Conclusion Despite being conducted on a relatively small cohort, this exploratory study demonstrates that urinary metabolomics, particularly when combining NMR and MS datasets, holds promise as a predictive tool for renal function evolution in kidney transplant recipients. These findings support further validation in larger, independent cohorts.