Mechanisms and Medication Rules of Type 2 Diabetes Mellitus Based on Data Mining and Network Pharmacology / 中国实验方剂学杂志

ABSTRACT

Objective: To analyze the medication rules of traditional Chinese medicine (TCM) in treating type 2 diabetes mellitus (T2DM),and explore the mechanism of action.Method: The articlesabout TCM treatment for T2DM in CNKI and Wanfang Med Online were retrieved.The data was entered into Excel 2013 to set up a database,and the factor analysis,association rules and cluster analysis were used to analyze core medication combinations by SPSS Statistics 19.0 and SPSS Modeler 14.1.kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed using BATMAN-TCM to reveal the mechanism of action.Result: A total of 92 prescriptions were included,the high frequency medicines were Astragali Radix.The major properties of TCM were cold and plain.The major flavors of TCM were sweet and bitter.The major meridian tropism of TCM was livermeridian.These high frequency medicines were deficiency-nourishing herbs,and blood circulation-invigorating and stasis-eliminating herbs.Totally 6 core medication combinations were mined through association rules.Seven common factors were extracted from factor analysis.Clustering analysis was used to get 6 kinds of herbs.KEGG pathway analysis revealed 24 biological pathways and 6 diseases relating to T2DM.Conclusion: The most commonly used Astragali Radix for treating T2DM.In clinical,it can be combined with Poria,Salviae Miltiorrhizae Radix et Rhizoma and Rehmanniae Radix for treating T2DM.At the same time,it can be combined with such herbs as deficiency-nourishing herbs,and blood circulation-invigorating and stasis-eliminating herbs.The mechanism of action of high-frequency herbs may be related to the targets of exophosphatase/phosphodiesterase (PDEs),adenosine A1 receptor gene 1(ADORA1),adenosine A2A receptor gene (ADORA2A),neuropetide second receptor (NPY2R),and endothelial carbon monoxide synthase (NOS3) by interfering with PI3K/Akt,starch and sucrose metabolism signaling pathways.