Serum metabolomics identifies gut-derived uremic toxins and bile acid dysregulation associated with chronic kidney disease severity

Chronic kidney disease (CKD) affects more than 700 million people worldwide, however conventional biomarkers like creatinine cannot identify early-stage disease or accurately predict progression. In this study, untargeted and targeted metabolomic approaches were combined to identify novel biomarkers relevant for CKD staging and early detection in an underrepresented Egyptian population. Untargeted ultra-high-performance liquid chromatography–mass spectrometry analyses in both ionization modes were performed on serum samples of 50 CKD patients 25 early-stage CKD (eCKD), 25 end-stage kidney disease (ESKD) and 20 controls. Differential metabolites were determined by univariate and multivariate analyses, coupled with pathway analysis and correlations with estimated glomerular filtration rate (eGFR). Five discriminating metabolites (p-hydroxyphenyllactic acid, indoxyl sulfate, xanthurenic acid, trimethylamine-N-oxide, and glycochenodeoxycholate) were subjected to targeted LC-MS/MS validation in an independent cohort (35 eCKD, 35 ESKD, 15 controls). Gut-derived uremic toxins, bile acid and tryptophan–kynurenine metabolic dysregulation were associated with CKD severity. p-hydroxyphenyllactic acid, xanthurenic acid, glutamyl-valine and indoxyl sulfate showed strong inverse correlations with eGFR (r = -0.75 to -0.85). A five-metabolite panel (indoxyl sulfate, p-hydroxyphenyllactic acid, trimethylamine-N-oxide, glycochenodeoxycholate, xanthurenic acid), demonstrated superior discriminatory performance compared with creatinine alone for distinguishing ESKD, especially indoxyl sulfate and p-hydroxyphenyllactic acid (AUC 0.847 and 0.828, respectively vs. 0.688). This first comprehensive metabolomics study in Egyptian CKD patients identifies alterations in gut microbiome–derived metabolites and bile acid metabolism associated with CKD severity. The multi-metabolite panel demonstrates potential for non-invasive discrimination between CKD stages and supports future longitudinal metabolomic studies aimed at improving CKD risk stratification and patient management.