Nanjing University of Technology, China.
Biomarkers. 2010 May;15(3):205-16. doi: 10.3109/13547500903419049.
This study describes the metabolic profiles of the development of hyperlipidaemia in a rat model, utilizing metabonomics by gas chromatography-mass spectrometry (GC-MS) determination coupled with multivariate statistical analysis. Rat plasma samples were collected before and during a high-lipid diet at days 0, 7, 14, 21 and 28, and were analysed for lipid levels using kit assays or metabonomics using GC-MS. Forty-one endogenous metabolites were separated, identified and quantified using GC-MS. The data matrix was processed by principal component analysis or partial least squares discriminant analysis. Dynamic modification of the rat metabonome can be clearly identified and tracked at different stages of hyperlipidaemia in the rat model. Potential biomarkers, including beta-hydroxybutyrate, tyrosine and creatinine, were identified. The current work suggests that metabonomics is able to provide an overview of biochemical profiles of disease progress in animal models. Using a metabonomic approach to identify physiopathological states promises to establish a new methodology for the early diagnosis of human diseases.
本研究采用气相色谱-质谱联用(GC-MS)结合多元统计分析的代谢组学方法,描述了高脂血症大鼠模型发展过程中的代谢特征。在高脂饮食第 0、7、14、21 和 28 天,收集大鼠血浆样本,使用试剂盒测定或 GC-MS 代谢组学方法分析脂质水平。使用 GC-MS 分离、鉴定和定量了 41 种内源性代谢物。通过主成分分析或偏最小二乘判别分析处理数据矩阵。在大鼠模型高脂血症的不同阶段,可以清楚地识别和跟踪大鼠代谢组的动态变化。鉴定出潜在的生物标志物,包括β-羟丁酸、酪氨酸和肌酸。本研究表明,代谢组学能够提供动物模型疾病进展的生化概况概述。使用代谢组学方法来识别生理病理状态有望为人类疾病的早期诊断建立一种新的方法。