The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*

OBJECTIVES: To identify the key genes and immunological pathways shared by type 2 diabetes mellitus (T2DM) and chronic obstructive pulmonary disease (COPD) and explore the potential therapeutic targets of T2DM complicated by COPD. METHODS: GEO database was used for analyzing the gene expression profiles in T2DM and COPD to identify the common differentially expressed genes (DEGs) in the two diseases. A protein-protein interaction network was constructed to identify the candidate hub genes, which were validated in datasets and disease sets to obtain the target genes. The diagnostic accuracy of these target genes was assessed with ROC analysis, and their expression levels and association with pulmonary functions were investigated using clinical data and blood samples of patients with T2DM and COPD. The abundance of 22 immune cells was analyzed with CIBERSORT algorithm, and their relationship with the target genes was examined using correlation analysis. DGIdb database was used for analyzing the drug-gene interactions and the druggable genes followed by gene set enrichment analysis. RESULTS: We identified a total of 175 common DEGs in T2DM and COPD, mainly enriched in immune- and inflammation-related pathways. Among these genes, CXCL12 was identified as the final target gene, whose expression was elevated in both T2DM and COPD (P<0.05) and showed good diagnostic efficacy. Immune cell infiltration correlation analysis showed significant correlations of CXCL12 with various immune cells (P<0.01). GESA analysis showed that high CXCL12 expression was significantly correlated with "cytokine-cytokine receptor interaction". Drug-gene analysis showed that most of CXCL12-related drugs were not targeted drugs with significant cytotoxicity. CONCLUSIONS CXCL12 is a potential common key pathogenic gene of COPD and T2DM, and small-molecule targeted drugs against CXCL12 can provide a new strategy for treatment T2DM complicated by COPD.
Humans ; Pulmonary Disease, Chronic Obstructive/complications* ; Diabetes Mellitus, Type 2/genetics* ; Chemokine CXCL12/metabolism* ; Protein Interaction Maps ; Gene Expression Profiling

OBJECTIVES: To investigate the effects of live combined Bacillus subtilis and Enterococcus faecium (LCBE) on glucose and lipid metabolism in mice with type 2 diabetes mellitus (T2DM) and circadian rhythm disorder (CRD) and explore the possible mechanisms. METHODS: KM mice were randomized into normal diet (ND) group (n=8), high-fat diet (HFD) group (n=8), and rhythm-intervention with HFD group (n=16). After 8 weeks of feeding, the mice were given an intraperitoneal injection of streptozotocin (100 mg/kg) to induce T2DM. The mice in CRD-T2DM group were further randomized into two equal groups for treatment with LCBE (225 mg/kg) or saline by gavage; the mice in ND and HFD groups also received saline gavage for 8 weeks. Blood glucose level of the mice was measured using a glucometer, and serum levels of Bmal1, PER2, insulin, C-peptide and lipids were determined with ELISA. Colon morphology and hepatic lipid metabolism of the mice were examined using HE staining and Oil Red O staining, respectively, and fecal short-chain fatty acids (SCFAs) was detected using LC-MS; GPR43 and GLP-1 expression levels were analyzed using RT-qPCR and Western blotting. RESULTS: Compared with those in CRD-T2DM group, the LCBE-treated mice exhibited significant body weight loss, lowered levels of PER2, insulin, C-peptide, total cholesterol (TC) and LDL-C, and increased levels of Bmal1 and HDL-C levels. LCBE treatment significantly increased SCFAs, upregulated GPR43 and GLP-1 expressions at both the mRNA and protein levels, and improved hepatic steatosis and colon histology. CONCLUSIONS LCBE ameliorates lipid metabolism disorder in CRD-T2DM mice by reducing body weight and improving lipid profiles and circadian regulators possibly via the SCFAs/GPR43/GLP-1 pathway.
Animals ; Mice ; Lipid Metabolism ; Diabetes Mellitus, Type 2/metabolism* ; Enterococcus faecium ; Glucagon-Like Peptide 1/metabolism* ; Bacillus subtilis ; Diabetes Mellitus, Experimental/metabolism* ; Circadian Rhythm ; Blood Glucose/metabolism* ; Receptors, G-Protein-Coupled/metabolism* ; Fatty Acids, Volatile/metabolism* ; Male ; Chronobiology Disorders/metabolism*

Parkinson's disease (PD), a chronic and common neurodegenerative disease, is characterized by the progressive loss of dopaminergic neurons in the dense part of the substantia nigra and abnormal aggregation of alpha-synuclein. Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by chronic insulin resistance and deficiency in insulin secretion. Extensive evidence has confirmed shared pathogenic mechanisms underlying PD and T2DM, such as oxidative stress caused by insulin resistance, mitochondrial dysfunction, inflammation, and disorders of energy metabolism. Conventional drugs for treating T2DM, such as metformin and glucagon-like peptide-1 receptor agonists, affect nerve repair. Even drugs for treating PD, such as levodopa, can affect insulin secretion. This review summarizes the relationship between PD and T2DM and related therapeutic drugs from the perspective of insulin signaling pathways in the brain.
Humans ; Parkinson Disease/drug therapy* ; Diabetes Mellitus, Type 2/pathology* ; Signal Transduction/physiology* ; Receptor, Insulin/metabolism* ; Animals ; Insulin Resistance/physiology* ; Insulin/metabolism*

Coptis chinensis Franch. and Panax ginseng C. A. Mey. are traditional herbal medicines with millennia of documented use and broad therapeutic applications, including anti-diabetic properties. However, the synergistic effect of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng on type 2 diabetes mellitus (T2DM) and its underlying mechanism remain unclear. The research demonstrated that the optimal ratio of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng was 4∶1, exhibiting maximal efficacy in improving insulin resistance and gluconeogenesis in primary mouse hepatocytes. This combination demonstrated significant synergistic effects in improving glucose tolerance, reducing fasting blood glucose (FBG), the weight ratio of epididymal white adipose tissue (eWAT), and the homeostasis model assessment of insulin resistance (HOMA-IR) in leptin receptor-deficient (db/db) mice. Subsequently, a T2DM liver-specific network was constructed based on RNA sequencing (RNA-seq) experiments and public databases by integrating transcriptional properties of disease-associated proteins and protein-protein interactions (PPIs). The network recovery index (NRI) score of the combined treatment group with a 4∶1 ratio exceeded that of groups treated with individual components. The research identified that activated adenosine 5'-monophosphate-activated protein kinase (AMPK)/acetyl-CoA carboxylase (ACC) signaling in the liver played a crucial role in the synergistic treatment of T2DM, as verified by western blot experiment in db/db mice. These findings demonstrate that the 4∶1 combination of total alkaloids from Coptis chinensis and total ginsenosides from Panax ginseng significantly improves insulin resistance and glucose and lipid metabolism disorders in db/db mice, surpassing the efficacy of individual treatments. The synergistic mechanism correlates with enhanced AMPK/ACC signaling pathway activity.
Animals ; Panax/chemistry* ; Ginsenosides/administration & dosage* ; Diabetes Mellitus, Type 2/metabolism* ; Mice ; Male ; Alkaloids/pharmacology* ; Coptis/chemistry* ; Drug Synergism ; Insulin Resistance ; Mice, Inbred C57BL ; Humans ; Transcriptome/drug effects* ; Blood Glucose/metabolism* ; Hypoglycemic Agents/administration & dosage* ; Drugs, Chinese Herbal/administration & dosage* ; Hepatocytes/metabolism*

BACKGROUND: Alpha-glucosidase inhibitors or dipeptidyl peptidase-4 inhibitors are both hypoglycemia agents that specifically impact on postprandial hyperglycemia. We compared the effects of acarbose and sitagliptin add on to metformin on time in range (TIR) and glycemic variability (GV) in Chinese patients with type 2 diabetes mellitus through continuous glucose monitoring (CGM). METHODS: This study was a randomized, open-label, active-con-trolled, parallel-group trial conducted at 15 centers in China from January 2020 to August 2022. We recruited patients with type 2 diabetes aged 18-65 years with body mass index (BMI) within 19-40 kg/m 2 and hemoglobin A1c (HbA1c) between 6.5% and 9.0%. Eligible patients were randomized to receive either metformin combined with acarbose 100 mg three times daily or metformin combined with sitagliptin 100 mg once daily for 28 days. After the first 14-day treatment period, patients wore CGM and entered another 14-day treatment period. The primary outcome was the level of TIR after treatment between groups. We also performed time series decomposition, dimensionality reduction, and clustering using the CGM data. RESULTS: A total of 701 participants received either acarbose or sitagliptin treatment in combination with metformin. There was no statistically significant difference in TIR between the two groups. Time below range (TBR) and coefficient of variation (CV) levels in acarbose users were significantly lower than those in sitagliptin users. Median (25th percentile, 75th percentile) of TBR below target level <3.9 mmol/L (TBR 3.9 ): Acarbose: 0.45% (0, 2.13%) vs . Sitagliptin: 0.78% (0, 3.12%), P = 0.042; Median (25th percentile, 75th percentile) of TBR below target level <3.0 mmol/L (TBR 3.0 ): Acarbose: 0 (0, 0.22%) vs . Sitagliptin: 0 (0, 0.63%), P = 0.033; CV: Acarbose: 22.44 ± 5.08% vs . Sitagliptin: 23.96 ± 5.19%, P <0.001. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV (group with small wave, moderate wave and big wave). No significant difference was found in the complexity of glucose time series index (CGI) between acarbose users and sitagliptin users. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV. CONCLUSIONS: Acarbose had slight advantages over sitagliptin in improving GV and reducing the risk of hypoglycemia. Time series analysis of CGM data may predict GV and the risk of hypoglycemia. TRIAL REGISTRATION Chinese Clinical Trial Registry: ChiCTR2000039424.
Humans ; Metformin/therapeutic use* ; Sitagliptin Phosphate/therapeutic use* ; Acarbose/therapeutic use* ; Diabetes Mellitus, Type 2/blood* ; Middle Aged ; Male ; Female ; Adult ; Blood Glucose/drug effects* ; Hypoglycemic Agents/therapeutic use* ; Aged ; Glycated Hemoglobin/metabolism* ; Adolescent ; Young Adult ; China ; East Asian People

BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical foundation for enhancing diagnosis and treatment. METHODS: Gene expression profiles from liver tissues of three well-characterized gluconeogenesis mouse models were analyzed to identify commonly differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA), machine learning techniques, and diagnostic tests on transcriptome data from publicly available datasets of type 2 diabetes mellitus (T2DM) patients were employed to assess the clinical relevance of these DEGs. Subsequently, we identified hub biomarkers associated with gluconeogenesis-related glycometabolic disorders, investigated potential correlations with immune cell types, and validated expression using quantitative polymerase chain reaction in the mouse models. RESULTS: Only a few common DEGs were observed in gluconeogenesis-related glycometabolic disorders across different contributing factors. However, these DEGs were consistently associated with cytokine regulation and oxidative stress (OS). Enrichment analysis highlighted significant alterations in terms related to cytokines and OS. Importantly, osteomodulin ( OMD ), apolipoprotein A4 ( APOA4 ), and insulin like growth factor binding protein 6 ( IGFBP6 ) were identified with potential clinical significance in T2DM patients. These genes demonstrated robust diagnostic performance in T2DM cohorts and were positively correlated with resting dendritic cells. CONCLUSIONS Gluconeogenesis-related glycometabolic disorders exhibit considerable heterogeneity, yet changes in cytokine regulation and OS are universally present. OMD , APOA4 , and IGFBP6  may serve as hub biomarkers for gluconeogenesis-related glycometabolic disorders.
Machine Learning ; Humans ; Computational Biology/methods* ; Biomarkers/metabolism* ; Diabetes Mellitus, Type 2/genetics* ; Animals ; Mice ; Gluconeogenesis/physiology* ; Gene Expression Profiling ; Transcriptome/genetics* ; Gene Regulatory Networks/genetics* ; Clinical Relevance

Diabetes mellitus (DM) is a major global health issue, with glycated hemoglobin levels serving as the gold standard for evaluating glucose level control in DM patients. However, it has limitations in reflecting glucose oscillations (i.e. glycemic variability, GV). Increasing evidence suggests that GV is closely related to the progression of diabetes complications and patient prognosis. As people realize the importance of avoiding hypoglycemia while achieving target glycated hemoglobin levels in treatment, the clinical significance of GV becomes more obvious. This article systematically reviewed the concept and connotation of GV, summarized the latest research on its role in the complications of diabetes, and revealed the biochemical and pathophysiological abnormalities caused by excessive glycemic oscillation, aiming to provide a theoretical basis for the risk warning and early intervention of DM patients.
Humans ; Blood Glucose/metabolism* ; Diabetes Complications/physiopathology* ; Glycated Hemoglobin/metabolism* ; Hypoglycemia ; Diabetes Mellitus, Type 2/complications*

The effect of Huanglian Jiedu Decoction on the intestinal flora of type 2 diabetes mellitus(T2DM) was investigated using 16S rRNA sequencing technology. Sixty rats were randomly divided into a normal group(10 rats) and a modeling group(50 rats). After one week of adaptive feeding, a high-fat diet + streptozotocin was given for modeling, and fasting blood glucose >16.7 mmol·L~(-1) was considered a sign of successful modeling. The modeling group was randomly divided into the model group, high-, medium-, and low-dose groups of Huanglian Jiedu Decoction, and metformin group. After seven days of intragastric treatment, the feces, colon, and pancreatic tissue of each group of rats were collected, and the pathological changes of the colon and pancreatic tissue of each group were observed by hematoxylin-eosin staining. The changes in the intestinal flora structure of each group were observed by the 16S rRNA sequencing method. The results showed that compared with the model group, the high-, medium-, and low-dose of Huanglian Jiedu Decoction reduced fasting blood glucose levels to different degrees and showed no significant changes in body weight. The number of islet cells increased, and intestinal mucosal damage attenuated. Alpha diversity analysis revealed that Huanglian Jiedu Decoction reduced the abundance and diversity of intestinal flora in rats with T2DM; at the phylum level, low-and mediam-dose of Huanglian Jiedu Decoction reduced the abundance of Bacteroidota, Proteobacteria, and Desulfobacterota and increased the abundance of Firmicute and Bacteroidota/Firmicutes, while the high-dose of Huanglian Jiedu Decoction increased the relative abundance of Proteobacteria and Bacteroidota/Firmicutes ratio, and decreaseal the relative; abundance of Firmicute; at the genus level, Huanglian Jiedu Decoction increased the relative abundance of Allobaculum, Blautia, and Lactobacillus; LEfse analysis revealed that the biomarker of low-and medium-dose groups of Huanglian Jiedu Decoction was Lactobacillus, and the structure of the intestinal flora of the low-dose group of Huanglian Jiedu Decoction was highly similar to that of the metformin group. PICRUSt2 function prediction revealed that Huanglian Jiedu Decoction mainly affected carbohydrate and amino acid metabolic pathways. It suggested that Huanglian Jiedu Decoction could reduce fasting blood glucose and increase the number of islet cells in rats with T2DM, and its mechanism of action may be related to increasing the abundance of short-chain fatty acid-producing strains and Lactobacillus and affecting carbohydrate and amino acid metabolic pathways.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Diabetes Mellitus, Type 2/metabolism* ; Gastrointestinal Microbiome/drug effects* ; Rats ; Male ; Rats, Sprague-Dawley ; Humans ; Bacteria/drug effects* ; Blood Glucose/metabolism*

Type 2 diabetes mellitus(T2DM) is a prevalent metabolic and endocrine disorder. Long-term hyperglycemia can lead to severe chronic complications, imposing substantial economic burdens on both society and patients. Despite the availability of various hypoglycemic agents for clinical use, these agents often fail to meet the therapeutic needs of T2DM and its complications. Consequently, there is an urgent need for novel therapeutic strategies and drugs. Lithocarpus litseifolius(L. litseifolius), commonly referred to as "cordyceps on trees", has a long history of use in traditional medicine and can be applied in tea, sugar, and medicine. Research indicates that L. litseifolius extracts are rich in dihydrochalcones, including trilobatin, phloridzin, and phloretin, which exhibit a range of pharmacological activities, such as anti-inflammatory, antioxidant, hypoglycemic, hypolipidemic, hepatoprotective, and cardioprotective effects. These properties suggest potential applications in the treatment of T2DM and its complications. This review systematically compiled and organized the relevant literature from the past decade on dihydrochalcones(trilobatin, phloridzin, and phloretin) from L. litseifolius extracts. It highlighted recent research progress regarding their role in treating T2DM and its complications through mechanisms such as reducing insulin resistance, regulating glucose transport, improving glucose and lipid metabolism, modulating enzyme activity, regulating gut microbiota, and alleviating inflammation and oxidative damage. The purpose of this review is to provide a reference and basis for future research on the prevention and treatment of T2DM and its complications using dihydrochalcones(trilobatin, phloridzin, and phloretin) from L. litseifolius extracts.
Chalcones/chemistry* ; Diabetes Mellitus, Type 2/metabolism* ; Humans ; Animals ; Elaeocarpaceae/chemistry* ; Drugs, Chinese Herbal/therapeutic use* ; Hypoglycemic Agents/chemistry* ; Plant Extracts/chemistry*