代谢组学在糖尿病肾病患者诊断中的应用

Metabonomic Analysis in the Diagnosis of Diabetic Nephropathy

目的建立糖尿病肾病(DN)患者血清代谢物特征谱图。方法收集108例血清标本,其中DN(1～4期)患者80例、健康对照者28例。应用基于核磁共振(1H NMR)的代谢组学方法,通过CMPG波谱获取数据,并结合PCA和PLS-DA模式识别分析法对数据进一步分析处理。结果成功建立DN代谢物特征谱图,血清1H CPMG谱图主要峰值差异集中在0.5～4.5 ppm区域。超过15种主要代谢物可被1H NMR谱图识别,与健康对照者相比,DN患者血清中乳酸水平明显升高,脂质、葡萄糖、胆碱、磷酸胆碱、甘油磷酸胆碱、甜菜碱、牛磺酸和鲨肌醇水平明显下降。通过此种方法可明显区分DN(1～4期)患者与健康对照者。结论基于NMR的代谢组学技术结合模式识别分析方法可有效检测DN患者血清代谢物特征谱图的改变,有望成为诊断和分级的新方法。

Objective To establish characteristic spectra of serum metabolites in patients with diabetic nephropathy （DN）. Methods The serum samples of 80 patients with DN （ I - 4 stage） and 28 health controls were collected. The 1H NMR spectroscopy - based metabolomic approach and CMPG spectrum were applied for data assessment, which were further processed by pattern recognition analysis including principal component analysis （ PCA ） and partial least squares - discriminant analysis （PLS - DA）. Results The characteristic spectrum of serum metabolites in DN was successfully established. The specific serum 1H CPMG spectrum peak in the 0.5 -4.5ppm area was observed. More than 15 major metabolites could be recognized by 1H - NMR. Compared with healthy controls, serum lactate was significantly up -regulated, while lipid, glucose, choline, phosphoric acid choline, glycerophosphoric acid choline, betaine, tanrine and scyllitol levels were significantly down - regulated in DN patients （ P 〈 0. 05 ）. By this method, DN was diagnosed. Conclusion Combination of NMR - based metabolomics technologies and pattern recognition analysis can detect serum metabolites spectrum changes of diabetic chronic kidney disease effectively, suggesting a novel method for diagnosis and classification of DN.