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目的:通过分析原发性肾病综合征(PNS)患儿血浆代谢物,构建与PNS相关的代谢途径网络,发现潜在的生物标志物以辅助临床诊断。方法:采用气相色谱质谱联用(GC-MS)技术分析22例PNS患儿及22例健康儿童血浆代谢物,利用预处理软件XCMS进行数据预处理,主成分分析(PCA)与偏最小二乘判别分析(PLS-DA)对两组差异性代谢产物进行分析。结果:构建了PCA与PLS-LDA模型,PCA模型中变量的可解释率和可预测率为43.6%和12.9%,PLS-LDA模型中变量的可解释率和可预测率分别为79.8%和59.2%,共筛选出15种差异性代谢物,主要有胆固醇、柠檬酸、亚油酸及氨基酸等,其中乳酸盐、亚油酸、软脂酸、丙酮酸、胆固醇含量升高,其余10种代谢物含量降低。结论:筛选出的15种差异性代谢物变化导致糖酵解、糖异生、脂类代谢和蛋白质代谢减弱,三羧酸循环紊乱。该研究为进一步明确PNS发病机制提供了新的科学依据,并有助于疾病诊断和预后评估。
OBJECTIVE: By analyzing plasma metabolites in children with primary nephrotic syndrome (PNS), we constructed a network of metabolic pathways related to PNS and found potential biomarkers to aid clinical diagnosis. Methods: Plasma metabolites of 22 children with PNS and 22 healthy children were analyzed by gas chromatography-mass spectrometry (GC-MS). Pretreatment software XCMS was used to preprocess the data. The principal component analysis (PCA) and partial least squares Discriminant Analysis (PLS-DA) Two groups of differential metabolites were analyzed. Results: The PCA and PLS-LDA models were constructed. The explanatory and predictive rates of variables in the PCA model were 43.6% and 12.9%, respectively. The explanatory and predictive rates of variables in the PLS-LDA model were 79.8% and 59.2%, respectively %, A total of 15 kinds of differential metabolites were screened, mainly cholesterol, citric acid, linoleic acid and amino acids, lactate, linoleic acid, palmitic acid, pyruvic acid, cholesterol content increased, the remaining 10 Metabolite content decreased. CONCLUSION: The changes of the 15 differentially selected metabolites lead to glycolysis, gluconeogenesis, lipid metabolism and protein metabolism weakened, and the tricarboxylic acid cycle is disturbed. The study provides a new scientific basis for further clarifying the pathogenesis of PNS and contributes to disease diagnosis and prognosis evaluation.