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目的初步研究新疆维吾尔族(维族)、哈萨克族(哈族)两民族糖尿病人群尿液代谢成分的特征,探讨核磁共振技术在糖尿病发病机制中的应用。方法收集尿液存于50ml接尿管中,置于-20℃冰箱中保存备用。核磁共振(NMR)测量前样品准备:尿液于4℃解冻,摇匀,在550μl尿液中加入55μl磷酸盐缓冲液,摇匀,4℃离心10min(10000r/min),取上清液550μl转入5mmNMR样品管中,测定核磁共振-维氢谱(1HNMR)。将得到的积分数据归一化后进行主成分分析。结果维族、哈族两民族中2型糖尿病(T2DM)组与正常空腹血糖(NFG)组的尿液代谢谱有明显差异,2组间含量差异明显的物质主要是葡萄糖和肌酐的变化。维族、哈族T2DM组尿样中牛磺酸、葡萄糖等代谢物有升高的趋势,而肌酐、马尿酸、二甲胺、乙酸、甲酸等代谢物有降低的趋势;维族空腹血糖受损(IFG)组尿样中柠檬酸、乳酸的含量高于T2DM组。哈族IFG组尿样中葡萄糖含量升高明显,而乳酸、柠檬酸含量变化不大。结论 1HNMR结合主成分分析(PCA)可以较好地呈现出不同空腹血糖水平人群的尿液代谢物特征。
Objective To study the characteristics of urinary metabolic components in two ethnic Uygur (Kazak Uygur) and Kazakh (Kazak ethnic groups) Xinjiang Uygur Autonomous Region (Uygur) and to explore the application of nuclear magnetic resonance (MR) technique in the pathogenesis of diabetes mellitus. Methods Urine collected in 50ml catheter, placed in -20 ℃ refrigerator for future use. Sample preparation before NMR measurement: The urine was thawed at 4 ° C and shaken. 55 μl of phosphate buffer was added to 550 μl of urine, and the resulting solution was centrifuged at 4 ° C for 10 min (10000 rpm). 550 μl of the supernatant Transferred to 5mmNMR sample tube, measured by nuclear magnetic resonance - dimensional hydrogen spectrum (1HNMR). The integral data obtained after normalization of the principal component analysis. Results There was a significant difference in urinary metabolites between type 2 diabetes mellitus (T2DM) group and normal fasting glucose (NFG) group in Uygur and Kazak ethnic groups. Changes of glucose and creatinine in 2 groups were significant. Uygur, Kazakh T2DM group of urine samples of taurine, glucose and other metabolites have an increasing trend, and creatinine, hippuric acid, dimethylamine, acetic acid, formic acid and other metabolites have a downward trend; Uygur impaired fasting glucose IFG) group of urine samples of citric acid, lactic acid content was higher than T2DM group. Kazakh IFG group urine glucose increased significantly, while the content of lactic acid, citric acid little change. Conclusion 1HNMR combined with principal component analysis (PCA) can better present the urine metabolites in different fasting blood glucose levels.