Nuclear Magnetic Resonance Metabolomic Profiling and Urine Chemistries in Incident Kidney Stone Formers Compared with Controls. Issue 11 (November 2022)

• Record Type: Journal Article
• Authors: Thongprayoon, Charat; Vuckovic, Ivan; Vaughan, Lisa E.; Macura, Slobodan; Larson, Nicholas B.; D'Costa, Matthew R.; Lieske, John C.; Rule, Andrew D.; Denic, Aleksandar
• Is Part Of: Journal of the American Society of Nephrology. Volume 33:Issue 11(2022)
• Page Start: 2071
• Page End: 2086
• Publication Date: 2022-11
• Subjects: metabolomics -- urine chemistries -- nephrolithiasis -- supersaturation -- NMR -- kidney calculi
• DOI: 10.1681/ASN.2022040416
• Languages: English
• ISSNs: 1046-6673

Abstract

Significance Statement: An altered balance of urine composition has been linked to stone formation, yet the exact mechanisms are still unclear. In this study, metabolomic analysis using two different methodologies of 24-hour urine samples from 418 incident stone formers and 440 controls demonstrated that several urine chemistries and nuclear magnetic resonance (NMR)–quantified metabolites differed significantly between the two groups. Addition of NMR-quantified metabolites did not significantly improve discrimination of stone formers beyond clinically measured urine chemistries, suggesting the urinary excretion of NMR-based metabolites and standard urine chemistries could reflect similar biological pathways. However, NMR did identify several novel metabolites, which may represent previously unrecognized pathways that could be further studied and therapeutically targeted. Visual Abstract: Abstract : Background: The urine metabolites and chemistries that contribute to kidney stone formation are not fully understood. This study examined differences between the urine metabolic and chemistries profiles of first-time stone formers and controls. Methods: High-resolution 1 H-nuclear magnetic resonance (NMR) spectroscopy-based metabolomic analysis was performed in 24-hour urine samples from a prospective cohort of 418 first-time symptomatic kidney stone formers and 440 controls. In total, 48 NMR-quantified metabolites in addition to 12 standard urine chemistries were assayed. Analysis of covariance was used to determine the association of stone former status with urine metabolites or chemistries after adjusting for age and sex and correcting for the false discovery rate. Gradient-boosted machine methods with nested cross-validation were applied to predict stone former status. Results: Among the standard urine chemistries, stone formers had lower urine oxalate and potassium and higher urine calcium, phosphate, and creatinine. Among NMR urine metabolites, stone formers had lower hippuric acid, trigonelline, 2-furoylglycine, imidazole, and citrate and higher creatine and alanine. A cross-validated model using urine chemistries, age, and sex yielded a mean AUC of 0.76 (95% CI, 0.73 to 0.79). A cross-validated model using urine chemistries, NMR-quantified metabolites, age, and sex did not meaningfully improve the discrimination (mean AUC, 0.78; 95% CI, 0.75 to 0.81). In this combined model, among the top ten discriminating features, four were urine chemistries and six NMR-quantified metabolites. Conclusions: Although NMR-quantified metabolites did not improve discrimination, several urine metabolic profiles were identified that may improve understanding of kidney stone pathogenesis.