Objective To explore the potential molecular mechanism of Rhodiola crenulata (RC) for type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (AD) by network pharmacology and molecular docking. Methods The target genes of T2DM and AD, the effective active components and targets of RC were identified through multiple public databases during March to August, 2022. The main active components and core genes of RC anti T2DM-AD were screened. The key genes were enrichment analyzed by gene ontology function and Kyoto gene and Kyoto Encyclopedia of Genes and Genomes. AutoDock Vina was used for molecular docking and binding energy calculation. Results A total of 5189 T2DM related genes and 1911 AD related genes were obtained, and the intersection result showed that there were 1418 T2DM-AD related genes. There were 48 active components of RC and 617 corresponding target genes. There were 220 crossing genes between RC and T2DM-AD. The main active components of RC anti T2DM-AD included kaempferol, velutin, and crenulatin. The key genes for regulation include ESR1, EGFR, and AKT1, which were mainly enriched in the hypoxia-inducible factor-1 signal pathway, estrogen signal pathway, and vascular endothelial growth factor signal pathway. The docking binding energies of the main active components of RC and key gene molecules were all less than −1.2 kcal/mol (1 kcal=4.2 kJ). Conclusions RC may play a role in influencing T2DM and AD by regulating the hypoxia-inducible factor-1 signaling pathway, estrogen signaling pathway, and vascular endothelial growth factor signaling pathway.
Objective To analyze the risk factors of type 2 diabetes mellitus and establish BP neural network model for screening of type 2 diabetes mellitus based on particle swarm optimization (PSO) algorithm. Methods Inpatients with type 2 diabetes mellitus in the Department of Endocrinology of the Affiliated Hospital of Guangdong Medical University and the Second Affiliated Hospital of Guangdong Medical University between July 2021 and August 2022 were selected as the case group and healthy people in the Health Management Center of the Affiliated Hospital of Guangdong Medical University as the control group. Basic information and physical and laboratory examination indicators were collected for comparative analysis. PSO-BP neural network model, BP neural network model and logistic regression models were established using MATLAB R2021b software and the optimal screening model of type 2 diabetes mellitus was selected. Based on the optimal model, the mean impact value algorithm was used to screen the risk factors of type 2 diabetes mellitus. Results A total of 1 053 patients were included in the case group and 914 healthy peoples in the control group. Except for type of salt, family history of comorbidities, body mass index, total cholesterol, low density lipoprotein cholesterol and staple food intake (P>0.05), the other indexes showed significant differences between the two groups. The performance of the PSO-BP neural network model outperformed the BP neural network model and the logistic regression model. Based on PSO-BP neural network model, the mean impact value algorithm showed that the risk factors for type 2 diabetes mellitus were fasting blood glucose , heart rate, age , waist-arm ratio and marital status , and the protective factors for type 2 diabetes mellitus were high density lipoprotein cholestero, vegetable intake, residence, education level, fruit intake and meat intake. Conclusions There are many influencing factors of type 2 diabetes mellitus. Focus should be placed on high-risk groups and regular disease screening should be carried out to reduce the risk of type 2 diabetes. The screening model of PSO-BP neural network performs the best, and it can be extended to the early screening and diagnosis of other diseases in the future.