Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease

Chronic kidney disease (CKD) is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications. The intricate interplay between kidney dysfunction and subsequent metabolic disturbances may provide insights into the underlying mechanisms driving CKD onset and progression. Herein, we proposed a large-scale plasma metabolite identification and quantification system that combines the strengths of targeted and untargeted metabolomics technologies, i.e., widely-targeted metabolomics (WT-Met) approach. WT-Met method enables large-scale identification and accurate quantification of thousands of metabolites. We collected plasma samples from 21 healthy controls and 62 CKD patients, categorized into different stages (22 in stages 1-3, 20 in stage 4, and 20 in stage 5). Using LC-MS-based WT-Met approach, we were able to effectively annotate and quantify a total of 1431 metabolites from the plasma samples. Focusing on the 539 endogenous metabolites, we identified 399 significantly altered metabolites and depicted their changing patterns from healthy controls to end-stage CKD. Furthermore, we employed machine-learning to identify the optimal combination of metabolites for predicting different stages of CKD. We generated a multiclass classifier consisting of 7 metabolites by machine-learning, which exhibited an average AUC of 0.99 for the test set. In general, amino acids, nucleotides, organic acids, and their metabolites emerged as the most significantly altered metabolites. However, their patterns of change varied across different stages of CKD. The 7-metabolite panel demonstrates promising potential as biomarker candidates for CKD. Further exploration of these metabolites can provide valuable insights into their roles in the etiology and progression of CKD.