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We aimed to identify urinary metabolic alterations in the pathogenesis of calcium oxalate nephrolithiasis which may provide possible therapeutics for kidney stone.
This retrospective study included 30 patients with calcium oxalate kidney stones and 30 controls, aged 18 to 70 years. The exclusion criteria were as follows: 1) with eGFR lower than 60 ml/min/1.73m2, or uric acid over 450 umol/L, or abnormal liver function; 2) diabetes or hypertension; 3) hepatitis B/C virus positive or with cancer; 4) acute inflammation. The 24 h urine of all the participants was collected and then analyzed by metabolomics using LC-MS. Clinical features were compared by Fisher’s exact test for categorical variables and Student’s t-test for the continuous variables. The correlation between metabolites and clinical features was investigated by Spearman’s correlation analysis.
Characteristics of participants
The stone formers had a mean age of 46.5 years at time of study, 40% female. There was no difference in age, gender, hemoglobin, kidney and liver function, blood glucose and cholesterol between groups. The 24h urine excretion of urea, creatinine, potassium, sodium, chlorine, phosphate and magnesium was significantly higher in the stone former group than in the control group (P<.05).
Alteration of the urinary metabolomics
We carried out targeted metabolomics to identify which urinary metabolites may be associated with calcium oxalate nephrolithiasis by UPLC-MS/MS. 300 metabolites, including organic acids, amino acids, carbohydrates, and short-chain fatty acids were identified. As PCA shown in Fig. 1A, the overall profile of metabolites was different between participants with calcium oxalate kidney stones and the non-stone formers. A volcano plot (Fig. 1B) shows the distribution of all detected features by P-value and fold-change indicating the significant metabolic divergence observed. A panel of 105 features was found to have significant differences (P<.05 and |log2FC| ≥0) in signal intensities between groups.
Nine metabolites that were most significantly different between participants with calcium oxalate kidney stones and the controls were identified (Fig. 2). N-Acetylglucosamine, gluconolactone, N-Acetylneuraminic acid, isocitric acid, ketoleucine, gluconic acid, quinolinic acid, ortho-hydroxyphenylacetic acid and ribulose and xylulose were less abundant in those who were stone formers versus controls. Azelaic acid was more abundant in the stone formers (P<.001).
Potential Biomarkers
A clear contrast between groups is observed when examining the relative intensity data for these significant features with a heatmap (Fig. 3A). Then, we performed a KEGG enrichment analysis to identify the altered metabolic pathways among the two groups. The results demonstrated that the signaling pathways involved phenylacetate, alanine, glycine and serine, aspartate metabolism, urea cycle and glucose-alanine cycle were enriched (Fig. 3B, 3C).
A plasma metabolomic signature offer a novel approach to characterize calcium oxalate stone formers.
