关键词: 泌尿系结石, 尿有机酸, 代谢组学, 泌尿外科

Abstract:

Objective: To systematically compare the differences in urinary organic acid metabolic profiles between patients with urinary calculi and non-calculi individuals, to screen disease-specific characteristic metabolic biomarkers and key signaling pathways, and to elucidate the potential mechanism underlying the occurrence and progression of urinary calculi at the metabolic level, so as to provide a theoretical basis for basic research and screening of intervention targets for urinary calculi. Methods: A total of 99 patients with urinary calculi and 57 non-calculi subjects were enrolled. Twenty-four-hour urine samples were collected and subjected to untargeted urinary organic acid metabolomics detection by gas chromatography-mass spectrometry (GC-MS). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to analyze the differences in metabolic profiles between the groups and the effects of clinical covariates. Volcano plot and variable importance in the projection (VIP) analysis were applied to screen differential organic acids. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database were used for metabolic pathway enrichment analysis. Machine learning algorithms were adopted to screen the most representative core metabolite combinations and verify the discriminatory efficacy of organic acids for disease status. Results: The urinary organic acid metabolic profiles showed a significant separation trend between the calculi group and the non-calculi group (OPLS-DA: R2X =0.139, R2Y =0.48, Q2 =0.457). Gender was an important covariate affecting metabolic profiles, while diabetes, hypertension and stone recurrence status exerted no significant influence. A total of 44 differential organic acids were screened (P < 0.05), including 6 upregulated and 38 downregulated metabolites. Ten core differential metabolites were identified via VIP analysis (P < 0.001, all downregulated). Differential organic acids were mainly enriched in energy metabolism pathways such as glyoxylate and dicarboxylate metabolism, as well as amino acid metabolism pathways including tryptophan metabolism. Among machine learning models, Gradient Boosting Machine and Random Forest exhibited the optimal efficacy with an average area under the curve (AUC) of 0.996. Five core metabolites including propionylglycine, 5-hydroxyindole-3-acetic acid, kynurenic acid, orotic acid and fumaric acid were identified as the optimal combination. The nomogram model constructed based on this combination yielded an AUC of 0.997; after calibration bias correction, the calibration curve was highly consistent with the ideal curve, with a mean absolute error of 0.022, which could effectively distinguish calculi patients from non-calculi individuals. Conclusion: Patients with urinary calculi present obvious disorders in urinary organic acid metabolism, which are mainly involved in energy and amino acid metabolic pathways and closely associated with the pathogenesis of urinary calculi. The five core metabolites (propionylglycine, 5-hydroxyindole-3-acetic acid, kynurenic acid, orotic acid and fumaric acid) can effectively distinguish calculi from non-calculi populations, and can serve as representative metabolic biomarkers to provide novel targets for subsequent mechanism exploration and targeted intervention.

Key words: Urolithiasis, Urinary organic acids, Metabolomics, Urology