Diabetic nephropathy (DN) is one of the most serious complications of diabetes and the main cause of end-stage renal failure. Rhubarb is a widely used traditional Chinese herb, and it has exhibited efficacy in reducing proteinuria, lowering blood sugar levels and improving kidney function in patients with DN. However, the exact pharmacological mechanism by rhubarb improves DN remain unclear due to the complexity of its ingredients. Hence, we systematically explored the underlying mechanisms of rhubarb in the treatment of DN. We adopted a network pharmacology approach, focusing on the identification of active ingredients, drug target prediction, gene collection, Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment. Molecular docking technology was used to verify the binding ability between the main active compounds and central therapeutic targets, and screen out the core active ingredients in rhubarb for the treatment of DN. Finally, molecular dynamics simulation was performed for the optimal core protein-ligand obtained by molecular docking using GROMACS software. The network analysis identified 16 active compounds in rhubarb that were linked to 37 possible therapeutic targets related to DN. Through protein–protein interaction analysis, TP53, CASP8, CASP3, MYC, JUN and PTGS2 were identified as the key therapeutic targets. By validation of molecular docking, finding that the central therapeutic targets have good affinities with the main active compounds of rhubarb, and rhein, beta-sitosterol and aloe-emodin were identified as the core active ingredients in rhubarb for the treatment of DN. Results from molecular dynamics simulations showed that TP53 and aloe-emodin bound very stably with a binding free energy of − 26.98 kcal/mol between the two. The results of the gene enrichment analysis revealed that the PI3K-Akt signalling pathway, p53 signalling pathway, AGE-RAGE signalling pathway and MAPK signalling pathway might be the key pathways for the treatment of DN, and these pathways were involved in podocyte apoptosis, glomerular mesangial cell proliferation, inflammation and renal fibrosis. Based on the network pharmacology approach and molecular docking technology, we successfully predicted the active compounds and their respective targets. In addition, we illustrated the molecular mechanisms that mediate the therapeutic effects of rhubarb against DN. These findings provided an important scientific basis for further research of the mechanism of rhubarb in the treatment of DN.

Background: Chronic kidney disease (CKD) has become a significant global public health challenge, which was reported to be highly correlated with the triglyceride glucose-body mass index (TyG-BMI). Nevertheless, literature exploring the association between changes in the TyG-BMI and CKD incidence is scant, with most studies focusing on individual values of the TyG-BMI. We aimed to investigate whether cumulative average in the TyG-BMI were associated with CKD incidence.Methods: Data in our study were obtained from the China Health and Retirement Longitudinal Study (CHARLS), which is an ongoing nationally representative prospective cohort study. The exposure was the cumulative average TyG-BMI from 2011 to 2015. The TyG-BMI was calculated by the formula ln [TG (mg/dl) × FBG (mg/dl)/2] × BMI (kg/m²), and the cumulative average TyG-BMI was calculated as follows: (TyG-BMI₂₀₁₁+ TyG-BMI₂₀₁₅)/2. Logistic regressions were used to determine the association between different quartiles of cumulative average TyG-BMI and CKD incidence. Meanwhile, restricted cubic spline was applied to examine the potential nonlinear association of the cumulative average TyG-BMI and CKD incidence. In addition, subgroup analysis was used to test the robustness of results.Results: Of the 6117 participants (mean [SD] age at baseline, 58.64 [8.61] years), 2793 (45.7%) were men. During the 4 years of follow-up, 470 (7.7%) incident CKD cases were identified. After adjusting for potential confounders, compared to the participants in the lowest quartile of cumulative average TyG-BMI, participants in the 3rd and 4th quartile had a higher risk of CKD onset. The ORs and 95%CIs were [1.509(1.147, 1.990)] and [1.452(1.085, 1.948)] respectively. In addition, restricted cubic spline showed the cumulative average TyG-BMI had a liner association (p-nonlinear = 0.139).Conclusions: The cumulative average in the TyG-BMI was independently associated with the risk of CKD in middle-aged and older adults. Monitoring long-term changes in the TyG-BMI may assist with the early identification of individuals at high risk of CKD.