ArticleKidney & blood pressure research2025
A New Perspective on Gas Chromatography-Mass Spectrometry Urinary Metabolomic Analysis and Efficient Risk Assessment of Urolithiasis: Morning Urine Organic Acid Profiles.
Article in Kidney & blood pressure research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Therapeutic efficacy of Annona muricata in counteracting nephrolithiasis-induced electrolyte imbalance and antioxidant disruption in ethylene glycol-treated rats.Scientific reports · 2026Article
- Machine learning-driven multi-omics integration of urinary organic acids and ions enables precision risk stratification for calcium oxalate nephrolithiasis.Frontiers in medicine · 2026Article
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17 authors.
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Abstract
introductionUrolithiasis is characterized by a high morbidity and recurrence rate, primarily attributed to metabolic disorders. The identification of more metabolic biomarkers would provide valuable insights into the etiology of stone formation and the assessment of disease risk. The present study aimed to seek potential organic acid (OA) biomarkers from morning urine samples and explore new methods based on machine learning (ML) for metabolic risk prediction of urolithiasis.
methodsMorning urine samples were collected from 117 healthy controls and 156 urolithiasis patients. Gas chromatography-mass spectrometry was used to obtain metabolic profiles. Principal component analysis and ML were carried out to screen robust markers and establish a prediction evaluation model.
resultsThere were 25 differential metabolites identified, such as palmitic acid, <sc>l</sc>-pyroglutamic acid, glyoxylate, and ketoglutarate, mainly involving arginine and proline metabolism, fatty acid degradation, glycine, serine, and threonine metabolism, glyoxylate and dicarboxylic acid metabolism. The urinary OA markers significantly improved the performance of the ML model. The sensitivity and specificity were up to 87.50% and 84.38%, respectively. The area under the receiver operating characteristic curve (AUC) was significantly improved (AUC = 0.9248).
conclusionThe results suggest that OA profiles in morning urine can improve the accuracy of predicting urolithiasis risk and possibly help understand the involvement of metabolic perturbations in metabolic pathways of stone formation and to provide new insights.
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