Multiplex Biomarker Approaches in Type 2 Diabetes Mellitus Research

Electrochemical biosensors for enhanced detection of diabetes mellitus

The worldwide incidence of diabetes mellitus (DM), as a long-term metabolic condition, continues to rise, creating an urgent need for accurate and efficient diagnostic methods to identify and treat the disease early. Among various analytical technologies, electrochemical biosensors stand out for their exceptional attributes, including precise detection, selective response, quick results, and affordable implementation. The current review study examines the latest developments in electrochemical biosensor technology designed specifically for diabetes detection, emphasizing novel approaches in blood sugar monitoring and tracking key diabetes indicators, including HbA1c, insulin levels, and ketones. The discussion encompasses cutting-edge developments such as sensors incorporating nanomaterials, non-enzymatic detection systems, and portable monitoring devices, emphasizing how these innovations improve both technical capabilities and patient experience. This review also demonstrates how next-generation electrochemical biosensors could fundamentally change diabetes care and monitoring, leading to more widely available and accurate disease tracking methods.

Exploring the Regulatory Mechanism of Modified Huanglian Maidong Decoction on Type 2 Diabetes Mellitus Biological Network Based on Systematic Pharmacology

Objective

To explore the mechanism of modified Huanglian Maidong decoction (Maidong-Sanqi-Huanglian Compounds, MSHCs) intervention in type 2 diabetes mellitus (T2DM).

Method

This study used PubChem and SciFinder to collect the molecular structure of MSHCs, used PharmMapper to predict the potential targets of MSHC, and combined them with the T2DM gene to construct MSHC-T2DM protein-protein interaction (PPI) network. The plugin MCODE in Cytoscape 3.7.1 was then used to perform cluster analysis on the MSHC-T2DM PPI network. The genes and targets were input into DAVID for Gene Ontology (GO) and pathway enrichment analysis. Finally, animal experiments were performed to verify the therapeutic effect of MSHC on T2DM.

Results

Several T2DM-related targets, clusters, signaling pathways, and biological processes are found. The experimental results showed that compared with the blank group, the content of fasting blood glucose (FBG) in the model group was higher (P < 0.01). Compared with the model group, the content of FBG decreased and the insulin level increased in the MSHC medium-dose (0.15 g/kg) and high-dose (0.45 g/kg) groups and metformin group after 4 weeks of drug administration (P < 0.05). MSHC can also improve blood liquid levels and inflammatory factor levels (P < 0.05).

Conclusion

MSHC may achieve therapeutic effects through regulating the T2DM-related targets, biological processes, and pathways, such as insulin resistance, energy metabolism, oxidative stress, and inflammation, found in this research.

Predictors of HbA1c among Adipocytokine Biomarkers in African-American Men with Varied Glucose Tolerance

This study explored adipocytokine associations with acute and chronic hyperglycemia in African-American men (AAM). Fourteen adipocytokines were measured from men with normal glucose tolerance (NGT) or type 2 diabetes (T2D, drug-naïve MF(-) or using metformin MF(+)). Acute and chronic hyperglycemia were evaluated by 120 min oral glucose tolerance test (OGTT) and glycohemoglobin A1c (HbA1c). AAM with T2D (n = 21) compared to NGT (n = 20) were older, had higher BMI and slightly higher glucose and insulin. In the fasted state, TNF-α, IL-6, PAI-1, IL-13, adiponectin, adipsin, and lipocalin were lower in T2D vs. NGT. At 120 min post-glucose load, TNF-α, IL-6, IL-13, IL-8, PAI-1, adiponectin, adipsin, lipocalin, and resistin were lower in T2D vs. NGT. There were no statistical differences for GM-CSF, IL-7, IL-10, IP-10, and MCP-1. Regression analysis showed that fasting IL-8, TNF-α, adiponectin, lipocalin, resistin, adipsin, and PAI-1 were associated with HbA1c. After adjusting for age, BMI, glucose tolerance, and metformin use, only adipsin remained significantly associated with HbA1c (p = 0.021). The model including adipsin, TNF-α, age, BMI, and group designation (i.e., NGT, MF(-), MF(+)) explained 86% of HbA1c variability. The data suggested that adipsin could be associated with HbA1c in AAM with varied glucose tolerance.