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BackgroundXuebifang (XBF), a potent Chinese herbal formula, has been employed in managing diabetic peripheral neuropathy (DPN). Nevertheless, the precise mechanism of its action remains enigmatic.PurposeThe primary objective of this investigation is to employ a bioinformatics-driven approach combined with network pharmacology to comprehensively explore the therapeutic mechanism of XBF in the context of DPN.Study design and MethodsThe active chemicals and their respective targets of XBF were sourced from the TCMSP and BATMAN databases. Differentially expressed genes (DEGs) related to DPN were obtained from the GEO database. The targets associated with DPN were compiled from the OMIM, GeneCards, and DrugBank databases. The analysis of GO, KEGG pathway enrichment, as well as immuno-infiltration analysis, was conducted using the R language. The investigation focused on the distribution of therapeutic targets of XBF within human organs or cells. Subsequently, molecular docking was employed to evaluate the interactions between potential targets and active compounds of XBF concerning the treatment of DPN.ResultsThe study successfully identified a total of 122 active compounds and 272 targets associated with XBF. 5 core targets of XBF for DPN were discovered by building PPI network. According to GO and KEGG pathway enrichment analysis, the mechanisms of XBF for DPN could be related to inflammation, immune regulation, and pivotal signalling pathways such as the TNF, TLR, CLR, and NOD-like receptor signalling pathways. These findings were further supported by immune infiltration analysis and localization of immune organs and cells. Moreover, the molecular docking simulations demonstrated a strong binding affinity between the active chemicals and the carefully selected targets.ConclusionIn summary, this study proposes a novel treatment model for XBF in DPN, and it also offers a new perspective for exploring the principles of traditional Chinese medicine (TCM) in the clinical management of DPN.

With improved standards of living, the incidence of multiple metabolic disorders has increased year by year, especially major risk factors for cardiovascular disease such as hyperglycemia and hyperlipidemia, continues to increase. Emerging epidemiological data and clinical trials have shown the additional protective effects of some metabolic therapy drugs against cardiovascular diseases. A series of studies have found that these drugs may work by modulating the composition of gut microbiota. In this review, we provide a brief overview of the contribution of the gut microbiota to both metabolic disorders and cardiovascular diseases, as well as the response of gut microbiota to metabolic therapy drugs with cardiovascular benefits. In this manner, we link the recent advances in microbiome studies on metabolic treatment drugs with their cardiovascular protective effects, suggesting that intestinal microorganisms may play a potential role in reducing cardiovascular risk factors. We also discuss the potential of microorganism-targeted therapeutics as treatment strategies for preventing and/or treating cardiovascular disease and highlight the need to establish causal links between therapeutics for metabolic diseases, gut microbiota modulation, and cardiovascular protection.

The present study sought to examine the therapeutic effect of a novel antidiabetic monomer combination (AMC) in treating type 2 diabetes mellitus (T2DM); while also elucidating the potential functional mechanism. Male C57BL/6J mice were fed a high-fat diet (HFD) for 12 weeks to establish T2DM. The AMC group showed significant reduction in weight, fasting blood glucose (FBG), serum total cholesterol (TC) and low density lipoprotein cholesterol (LDL-C), and experienced reduced insulin resistance based on oral glucose tolerance testing (OGTT) and hyperinsulinemic-euglycemic clamp testing (“gold standard” for determining in vivo insulin sensitivity). Further, AMC restored the altered intestinal flora by increasing the abundance of the beneficial bacteria Akkermansia, and decreasing the number of harmful bacteria, including Bacteroides, Odoribacter, Prevotella 9, Alistipes, and Parabacteroides. Components of the host-microbial metabolome were also significantly changed in the AMC group compared to the HFD group, including hydroxyphenyllactic acid, palmitoleic acid, dodecanoic acid, linoleic acid, and erucic acid. Furthermore, AMC was found to inhibit inflammation and suppress signaling pathways related to insulin resistance. Lastly, spearman correlation analysis revealed relationships between altered microbial community and co-metabolite levels, co-metabolites and inflammatory cytokines. Hence, the potential mechanism responsible for AMC-mediated alleviation of insulin resistance was suggested to be involved in modulation of bacteria-cometabolism-inflammation responses.