脂质代谢网络在代谢综合征及新药研究中的应用

Metabolic syndrome（MS） has aroused wide concern among the public in recent years. MS is a syndrome disease characterized by abnormal metabolism of the glucose and lipid, which comprises a cluster of abnormalities, with insulin resistance (IR) and adiposity as central features. It is the public health problem of the high incidence of MS in all over the world. The mechanism of MS is very complicated and until now it has not been completely clarified. In our work, a series of serum and urine samples will be analyzed, which from three different animal models of MS (including hereditary, buffet induction, stress combining high-fat and high-sugar diets induction) and the hypertension models and diabetes models, using the developed technique of the lipid metabolic network. By several different chemometrics methods (pattern recognition, etc.), we will obtain the differences of the lipid metabolic fingerprints between the model groups. At the same time, the relationship between the hypertension, diabetes and the development and progression of MS will be investigated. Based on creating data model, it will be researched that the characteristic metabolic pathway of the lipids. By the animal models of MS, we will investigate diformin(a blood-sugar decreasing medicine) and sGLP-1(a new drug in the development) treat MS and observe the influences of the lipid metabolic network. It will be investigated and compared that the variations of the lipid function and metabolic network between the different model groups after taking diformin or sGLP-1, to found the potential therapeutic targets and mechanism. This study could provide not only the new scientific data and thought for individualized prevention of MS, but also the new strategy and new mode for the new drugs screening.

代谢综合征（metabolic syndrome，MS）是以一系列糖脂代谢紊乱为特点的症候群性疾病，其发病率呈逐年攀升之势，已成为一项新的公共卫生问题。目前其发病机制尚未完全阐明。本课题拟通过已建立的脂质代谢网络技术平台，对三种MS模型、高血压模型及糖尿病模型的血清和尿液进行脂质代谢网络分析，使用模式识别等化学计量学方法，获得不同诱因MS模型的脂质差异谱及差异物，并考察高血压和糖尿病两大因素与MS发生、发展的关系，构建MS数据模型，推测相关的特征性脂质代谢途径。在动物模型基础上，研究现有的作用机理明确的药物（二甲双胍）及正在研发中的新药sGLP-1对MS大鼠模型脂质代谢网络的影响，考察和比较不同组别模型内源性脂质功能的变异以及脂质代谢网络的变化，寻找药物作用的潜在靶标及可能机制。本课题结果能够为MS的早期个体化防治提供新的科学数据与研究思路，也可为MS相关新药的筛选提供新策略和新模式。

本项目成功建立了代谢综合征（MS）大鼠模型，并利用脂质代谢网络分析技术，监测和评价了MS大鼠模型的内源性脂质代谢谱的变化，建立了适用于MS的脂质代谢网络分析方法和数据处理模式，初步确定MS模型大鼠血清的特征性脂质代谢表型，同时系统性评价了正常大鼠、MS模型、MS模型给予rhGLP-1组及MS模型给予sGLP-1组大鼠血清的脂质代谢轮廓差异，并通过多元统计分析和模式识别方法，初步识别了代谢综合征的脂质代谢标志物。在此基础上，对给予治疗药物后MS模型大鼠血清脂质代谢网络的差异进行了完整分析和评价，初步结果显示，两种待评价药物rhGLP-1与sGLP-1均能够降低模型动物血清中部分饱和游离脂肪酸的含量，可能与它们改善高血脂及高血糖症状相关联，而部分多不饱和脂肪酸如二十二碳六稀酸的变化可能与药物降低胰岛素抵抗的作用相关。但从脂质代谢网络的整体变化来看，rhGLP-1与sGLP-1的作用机理在脂代谢方面并非完全一致，具体的差异脂质代谢路径还有待于进一步的研究。本课题的研究结果有望为代谢综合征动物模型评价及相关新药的筛选提供新的策略和思路。