代谢综合征(简称MS)的发病率逐年上升且可以引起严重威胁生命的并发症，其中高血糖是诊断MS的主要组成部分（简称组分）。目前国内外对糖尿病、高血压等单一种类的慢性病研究很深入，而MS与多种慢性病相关且病因复杂，需要更为先进的技术和方法。本研究以课题组连续4年收集的3万多例健康体检人群资料、以及血糖单组分相关的7个糖基生物标志物为基础，以血糖代谢组分作为切入点,开展双向性队列研究；2014年采用整群抽样，前瞻性收集环境等因素及血液样本，按照血糖代谢组分基线时正常、临界、异常状况，构建链接分析动态预测模型；利用联合模型构建含血糖代谢两组分的动态关联模型；用糖基组学技术筛选含血糖代谢组分的MS病因中糖基生物标志物；再结合环境等因素，构建LASSO回归预测模型。其结果可为MS预防、开展人群健康管理提供科学依据，为糖基生物标志物作为MS早期诊断的指标提供理论基础，为同类研究提供方法学有意义的参考。

Metabolic syndrome (MS) refers to a group of inter-related risk factors that include dysglycemia, raised blood pressure (BP) and triglycerides (TG), low high-density lipoprotein cholesterol (HDL-C) levels, and obesity. MS can cause severe life-threatening complications and the incidence is going up year by year. The etiology of MS is complicated and associated to many chronic disease, at present, studies concerning on single disorders have been thoroughly researched , as hypertension, diabetes, etc. More methods and technologies are in need to have an exploration on MS.There were over 30,000 individuals participated in the annual physical examination in the past four years. We collected information on populatioin profile, blood glucose and glycosyl biomarker. The clinical information and blood sample from physical examination will be collected prospectively with cluster sampling. Based on the baseline information, blood glucose levels are classed as normal, marginal and abnormal to build link analysis model. Joint modeling is applied to build risk prediction model including blood glucose and other single component. Glycosylomics technology is used to find the glycosyl biomarkers of MS including blood glucose, combined with environment factors to build Lasso regression model. The result will provide the scientific evidence for prevention, treatment and health management of MS people. Finally, it will provide a kind of research method for other similar studies.