Study on the pathogenesis and prevention strategies of kidney stones based on GC–MS combined with metabolic pathway analysis

化学 气相色谱-质谱法 肾结石 代谢途径 色谱法 气相色谱法 质谱法 代谢组学 生物合成 溶剂 胆固醇 生物化学 内科学 新陈代谢 医学
作者
Haijie Xie,Rui Wang,Linguo Xie,Xian-Hua Wang,Chunyu Liu
出处
期刊:Rapid Communications in Mass Spectrometry [Wiley]
卷期号:36 (22) 被引量:1
标识
DOI:10.1002/rcm.9387
摘要

Most kidney stone composition analyses include organic compounds, and only few organic volatile compounds have been reported.In the present study, a novel approach for studying the pathogenesis and prevention of kidney stones was established. First, common organic volatile compounds were detected using gas chromatography-mass spectrometry (GC-MS) in 137 kidney stone samples. The Kyoto Encyclopedia of Genes and Genomes database and MetaboAnalyst 5.0 software were then used to analyze the metabolic pathways associated with the development of kidney stones.The metabolic pathway analysis of the common component cholesterol revealed that two metabolic pathways, the steroid biosynthesis pathway and the primary bile acid biosynthesis pathway, were closely associated with the formation of kidney stones. The pretreatment process for stone analysis, including the solvent type, solvent volume, and extraction time, was optimized to improve the detection efficiency. The calibration curve was y = 756 299x - 8 000 000, with a correlation coefficient (r) of 0.9992, which was obtained over the concentration range of 10-500 μg ml-1 of cholesterol. The recovery values of cholesterol ranged from 93.34% to 94.67%, 96.98% to 99.23%, and 87.27% to 93.00% when spiked with 0.75, 1.00, and 1.25 μg, respectively, with a relative standard deviation of no less than 3.18%. Finally, the content of common compounds was determined in 37 renal stone samples using the modified GC-MS method.The common organic volatile compound in the kidney stone samples detected using GC-MS was cholesterol, and the steroid biosynthesis and primary bile acid biosynthesis pathways were determined to be closely associated with the formation of kidney stones. The GC-MS method for detecting cholesterol in kidney stones was optimized for efficiency and accuracy.
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