A mini-network balance model for evaluating the progression of cardiovascular complications in Goto-Kakizaki rats

Abstract

Cardiovascular complications represent a leading cause of mortality in patients with type 2 diabetes mellitus (T2DM). During such complicated progression, subtle variations in the cardiovascular risk (CVR)-related biomarkers have been used to identify cardiovascular disease at the incipient stage. In this study we attempt to integrally characterize the progression of cardiovascular complications and to assess the beneficial effects of metformin combined with salvianolic acid A (Sal A), in Goto-Kakizaki (GK) rats with spontaneous T2DM. The rats were treated with metformin ( , ip) at ages from 8 to 22 weeks. During the treatment, the levels of asymmetric dimethylarginine, L-arginine, superoxide dismutase, malondialdehyde, glucose, high density lipoprotein and low density lipoprotein were assessed. Based on alterations in these biomarkers, a mini-network balance model was established using matrixes and vectors. Radar charts were created to visually depict the disruption of CVR-related modules (endothelial function, oxidative stress, glycation and lipid profiles). The description for the progression of cardiovascular disorder was quantitatively represented by u, the dynamic parameter of the model. The modeling results suggested that untreated GK rats tended to have more severe cardiovascular complications than the treatment groups. Metformin monotherapy retarded disease deterioration, whereas the combination treatment ameliorated the disease progression via restoring the balance. The current study, which focused on the balance of the mini-network and interactions among CVR-related modules, proposes a novel method for evaluating the progression of cardiovascular complications in T2DM as well as a more beneficial intervention strategy.