AimsIt is consensus that glucose variability (GV) plays an important role in maccomplications of type 2 diabetes, but whether GV has a causal role is not yet clear for cardiovascular disease (CVD). This study sought to explore the effect on GV for CVD risk factors with type 2 diabetes.MethodsThe systematic literature search was performed to identify all GV and CVD risk factors, including total cholesterol (TC), LDL cholesterol (LDL-C), triglyceride (TG), HDL cholesterol (HDL-C), Body Mass Index (BMI), waist circumference (WC), High-Sensitivity C-reactive protein (Hs-CRP), Homeostasis model assessment (HOMA) and carotid intima-media thickness (IMT). Preferred Reporting Items was synthesized for Systematic reviews and Meta Analyses guideline. And the pooled analyses were undertaken using Review Manager 5.3.ResultsTwenty two studies were included with a total of 1143 patients in high glucose variability group (HGVG) and 1275 patients low glucose variability group (LGVG). Among these selected CVD risk factors, HOMA-IR and reduced IMT were affected by GV. HOMA-IR level was significantly lower in LGVG than in HGVG (MD = 0.58, 95% CI: 0.26 to 0.91, P = 0.0004), with evidence of heterogeneity between studies (I2 = 0%; P = 0.47).Reduced IMT level was significantly lower in LGVG than in HGVG (SMD = 0.28, 95% CI: 0.09 to 0.47, P = 0.003), with evidence of heterogeneity between studies (I2 = 0%; P = 0.48). However, the others were no significant statistical difference.ConclusionsAmong these selected CVD risk factors in type 2 diabetes, minimizing GV could improve insulin resistance and reduced IMT, consistent with a lowering in risk of CVD.Electronic supplementary materialThe online version of this article (10.1186/s40200-017-0323-5) contains supplementary material, which is available to authorized users.
Aβ, tau, and P-tau have been widely accepted as reliable markers for Alzheimer's disease (AD). The crosstalk between these markers forms a complex network. AD may induce the integral variation and disruption of the network. The aim of this study was to develop a novel mathematic model based on a simplified crosstalk network to evaluate the disease progression of AD. The integral variation of the network is measured by three integral disruption parameters. The robustness of network is evaluated by network disruption probability. Presented results show that network disruption probability has a good linear relationship with Mini Mental State Examination (MMSE). The proposed model combined with Support vector machine (SVM) achieves a relative high 10-fold cross-validated performance in classification of AD vs. normal and mild cognitive impairment (MCI) vs. normal (95% accuracy, 95% sensitivity, 95% specificity for AD vs. normal; 90% accuracy, 94% sensitivity, 83% specificity for MCI vs. normal). This research evaluates the progression of AD and facilitates AD early diagnosis.
Purpose The study was designed to evaluate the disease outcome based on multiple biomarkers related to cerebral ischemia. Methods Rats were randomly divided into sham, permanent middle cerebral artery occlusion, and edaravone-treated groups. Cerebral ischemia was induced by permanent middle cerebral artery occlusion surgery in rats. To form a simplified crosstalk network, the related multiple biomarkers were chosen as S100β, HIF-1α, IL-1β, PGI2, TXA2, and GSH-Px. The levels or activities of these biomarkers in plasma were detected before and after ischemia. Concurrently, neurological deficit scores and cerebral infarct volumes were assessed. Based on a mathematic model, network balance maps and three integral disruption parameters (k, φ, and u) of the simplified crosstalk network were achieved. Results The levels or activities of the related biomarkers and neurological deficit scores were significantly impacted by cerebral ischemia. The balance maps intuitively displayed the network disruption, and the integral disruption parameters quantitatively depicted the disruption state of the simplified network after cerebral ischemia. The integral disruption parameter u values correlated significantly with neurological deficit scores and infarct volumes. Conclusion Our results indicate that the approach based on crosstalk network may provide a new promising way to integrally evaluate the outcome of cerebral ischemia.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.