To uncover and identify the hot topic and frontier of Chinese medicines treatment of type 2 diabetes mellitus (T2DM).

Web of Science ^TM was searched for published articles for Chinese medicines treatment of T2DM ranging from January 1st, 2002 to July 6th, 2016. Knowledge maps of the international Chinese medicines treatment of T2DM are visualized by using document co-occurrence analysis and word frequency analysis (Institution and Journal), co-citation clustering analysis (Co-reference), keyword co-occurrence clustering analysis with CiteSpac Ⅲ, a tool of scientometrics.

Universidad Nacional Autonoma de Mexico is the institution with the highest number of published papers that had been cited in this field, while China has four institutions among the top 10. The journal of the highest frequency of co-cited journal was Diabetes Care, a core one in the field. Keywords co-occurrence network was composed of 185 nodes, 541 lines, and divided into 10 clusters. Co-citation network of co-reference was composed of 407 nodes, 1199 lines, and divided into 20 clusters. Using Chinese medicine to improve insulin resistance and Chinese medicine research on blood glucose control are the hot topics. The frontier contains two aspects: new drugs development and application of intestinal insulin treatment and development and use of traditional Chinese medicine antidiabetic plants.

Institutions from China still plays a major role in TCM-focused T2DM studies. The effect of TCM herbs on insulin resistance is the hot topic of the domain. Developing new TCM herbal medicine that regulates incretin effect is the domain frontier. Research on the Chinese medicines treatment of T2DM needs more high-quality evidence to support, and its mechanism requires further exploration.

To propose and test a new approach based on community detection in the field of social computing for uncovering consensuses and treatment principles in traditional Chinese medicine (TCM).

Three Chinese databases (CNKI, VIP, and Wan Fang Data) were searched for published articles on TCM treatment of diabetic nephropathy (DN) from their inception until September 31, 2014. Zheng classification and herb data were extracted from included articles and used to construct a Zheng classification and treatment of diabetic nephropathy (DNZCT) network with nodes denoting Zhengs and herbs and edges denoting corresponding treating relationships among them. Community detection was applied to the DNZCT and detected community structures were analyzed.

A network of 201 nodes and 743 edges were constructed and six communities were detected. Nodes clustered in the same community captured the same semantic topic; different communities had unique characteristics, and indicated different treatment principles. Large communities usually represented similar points of view or consensuses on common Zheng diagnoses and herb prescriptions; small communities might help to indicate unusual Zhengs and herbs.

The results suggest that the community detection-based approach is useful and feasible for uncovering consensuses and treatment principles of DN treatment in TCM, and could be used to address other similar problems in TCM.