Objective To analyze the prevalence of leukemia in China from 1990 to 2019, predict the incidence, morbidity and mortality of leukemia in China from 2020 to 2040, and provides reference for the formulation of leukemia-related prevention and treatment strategies in China. Methods Based on the 2019 Global Burden of Disease database, the incidence, morbidity and mortality data of leukemia in China from 1990 to 2019 were collected, and the rate of change and annual estimated percentage of change (EAPC) were used to describe the epidemic trend of the disease. The Autoregressive Moving Average (ARIMA) model was used to predict the prevalence of leukemia in China from 2020 to 2040. Results In 2019, the age-standardized incidence, age-standardized prevalence and age-standardized mortality rate of leukemia in China decreased by 17.62%, 10.97%, and 41.56%, respectively, compared with 1990, and an average annual decrease of 1.06%, 0.89%, and 2.05%, respectively (P<0.05). From 1990 to 2019, the reduction age-standardized incidence rate, age-standardized prevalence rate and age-standardized mortality rate in Chinese women (EAPC was 1.56%, 1.38%, and 2.62%, respectively) was higher than that of men (EAPC was 0.61%, 0.43%, and 1.59%, respectively). In 2019, the incidence and prevalence were highest in the age group under 5 years of age, and the mortality rate was the highest in the age group over 80 years old. The prediction results of ARIMA model showed that the age-standardized incidence rate and prevalence of leukemia in China showed an increasing trend from 2020 to 2040, while the age-standardized mortality rate showed a decreasing trend. It is estimated that by 2040, the age-standardized incidence rate, age-standardized prevalence rate, and age-standardized mortality rate of leukemia will be 14.06/100 000, 108.23/100 000, and 2.83/100 000. Conclusions From 1990 to 2019, the age-standardized incidence rate, age-standardized prevalence rate and age-standardized mortality rate of leukemia in China decreased year by year, but they were still at a high level. The prediction results show that the age-standardized incidence rate and age-standardized prevalence rate of leukemia in China will continue to increase from 2020 to 2040, and it is necessary to continue to strengthen the surveillance, prevention and control of leukemia in the future.
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.
ObjectiveTo explore the metabolic changes during the differentiation of 3T3-L1 adipocytes caused by the treatment of the transient receptor potential vanilloid 4 (TRPV4)-specific agonist GSK1016790A basing on ultra-performance liquid chromatography-mass spectrometry technology. MethodsMouse 3T3-L1 cells were treated with GSK1016790A at different concentrations (0.1, 1, and 10 μmol/L), and the effect of drugs on cell proliferation was detected by cell counting kit-8 method. A mature adipocyte model was constructed, and GSK1016790A was used to activate TRPV4 channel protein activity and verify the expression levels of TRPV4 and triglycerides. Cell metabolites were collected for metabolomic studies, differential metabolites were screened between groups, and related metabolic pathways were analyzed. Results After GSK1016790A intervened in mature adipocytes, the expression levels of TRPV4 mRNA and triglycerides in cells were significantly upregulated (P<0.05). Metabolomics detection found that GSK1016790A screened a total of 45 differential metabolites such as 2-amino-1,3,4-octadecanetriol, linoleic acid, sphingosine, sphinganine, sn-glycerol-3-phosphate and uridine, mainly involving 13 possible metabolic pathways such as sphingolipid metabolism and biosynthesis of unsaturated fatty acids. Conclusion GSK1016790A may promote adipogenesis in adipocytes by activating TRPV4 channel protein activity, and at the same time participate in regulating metabolic pathways such as the biosynthesis of unsaturated fatty acids pathway and sphingolipid metabolism pathway, affecting lipid metabolism in adipocytes.
ObjectiveTo investigate the association between single nucleotide polymorphism (SNP) rs3754219 in the glucose transporters 1 (GLUT1) gene and genetic susceptibility to type 2 diabetes mellitus (T2DM) in Han population in Guangdong Province.MethodsA total of 1 092 T2DM patients (case group) and 1 092 healthy controls (control group) diagnosed or examined between November 2011 and October 2014 form 10 hospitals were enrolled in this study. SNPscanTM SNP classification technology was used to detect the polymorphism of rs3754219 of GLUT1 genetype. Finally, 1 067 T2DM patients and 1 054 healthy controls were included, removing 37 individuals with SNP typing deletion rates >20% and 26 individucals with failed SNP site genotyping. The differences in allele frequency distribution, genotype, and genetic models between the two groups were analyzed.ResultsAfter correction for age and body mass index, there was no statistically significant difference in allele frequency or polymorphism genotype frequency of rs3754219 (P>0.05). There was no statistically significant difference between the two groups under different genetic models (P>0.05).ConclusionGenetic susceptibility to T2DM in Han population in Guangdong Province may be unrelated to the GLUT1 rs3754219 SNP.
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.