糖尿病早期预警功能的动态网络生物标记的识别和研究

Diabetes mellitus is complex disease is harming the health of human in all over the world. And a lot of people have been suffering from the type 1 and type 2 diabetes. In this project, we focus on the type 1 diabetes and type 2 diabetes based on the theory of dynamic network biomarkers (DNB) which can be used to detect the early-warning signals of complex disease before the onset of this complex disease. The traditional biomarker cannot distinguish the pre-diabetes status from normal status due to the pre-diabetes status is similar with normal status in phenotype. So we want to develop a new algorithm to mine the potential dynamic network biomarkers in type 1 and type 2 diabetes which can distinguish the pre-diabetes status from the normal status by detecting the early-warning signals of the dynamic network biomarkers. Subsequently, the functional analysis of identified dynamic network biomarker would be done, and the dynamic changes of the regulation relationship among every phases of diabetes development would be recovered. The different interacting functional modules or metabolic pathways with dynamic network biomarkers of type 1 and type 2 diabetes in the different phases of diabetes development would be benefit to depict the mechanism of the onset of type 1 and type 2 diabetes.

糖尿病（Diabetes mellitus）是一个世界范围内严重影响人类健康的复杂疾病，该疾病在全世界范围内发病区域广、影响人数多。本项目以1型和2型糖尿病为研究对象，依据复杂疾病早期预警的动态网络生物标记（DNB）理论，并开发一种新的真对动态网络生物标记的算法，通过该算法能够准确的挖掘出糖尿病前疾病状态下的动态网络生物标记，利用该动态网络生物标记能够将与正常状态没有明显差别的前疾病状态从正常状态中区分出来。并通过识别该动态网络生物标记在糖尿病中的早期预警信号，而达到对糖尿病预防和早期诊断的作用。进而通过对所标识的两种类型糖尿病的动态网络生物标记的功能分析，以及它们在不同的疾病发生阶段与其它功能模块或代谢途径的调控关系的动态变化，进一步阐明两种糖尿病在分子水平上的发病机制。

本项目以动态网络生物标志物理论为基础，通过开发动态网络生物标志物的检测方法，对复杂疾病的早期预警信号进行检测。我们通过差异网络和单样本网络的构建等方法，开发了一系列通过计算方法检测动态网络生物标志物算法。其中，特别是基于单样本网络的动态网络生物标志物检测方法可以直接检测单个样本特异性的动态网络生物标志物，为其在精准医疗方面的应用奠定了理论基础。通过本项目的研究，我们共发表SCI论文5篇，另有一篇正在投稿，基本完成计划书中预期的任务。