Identifying subgroups of patients with type 2 diabetes based on real-world traditional chinese medicine electronic medical records - Zhao, Shuai; Li, Hengfei; Jing, Xuan; Zhang, Xuebin; Li, Ronghua; Li, Yinghao; Liu, Chenguang; Chen, Jie; Li, Guoxia; Zheng, Wenfei; Li, Qian; Wang, Xue; et al. (Frontiers in Pharmacology, 2023)
Type 2 diabetes (T2D) is a multifactorial complex chronic disease with a high prevalence worldwide, and T2D patients with different comorbidities often present multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of the clinical T2D population to help identify more accurate disease subtypes for personalized treatment. Here, utilizing the traditional Chinese medicine (TCM) clinical electronic medical records (EMRs) of 2137 T2D inpatients, we followed a heterogeneous medical record network (HEMnet) framework to construct heterogeneous medical record networks by integrating the clinical features from the EMRs, molecular interaction networks and domain knowledge. Of the 2137 T2D patients, 1347 were male (63.03%), and 790 were female (36.97%). Using the HEMnet method, we obtained 8 nonoverlapping patient subgroups. For example, in H3, Poria, Astragali Radix, Glycyrrhizae Radix et Rhizoma, Cinnamomi Ramulus, and Liriopes Radix were identified as significant botanical drugs. Cardiovascular diseases (CVDs) were found to be significant comorbidities. Furthermore, enrichment analysis showed that there were 6 overlapping pathways and 8 overlapping GO terms among the herbs, comorbidities, and T2D in H3. Our results demonstrate that identification of the T2D subgroup based on the HEMnet method can provide important guidance for the clinical use of herbal prescriptions and that this method can be used for other complex diseases.