BackgroundLarge language models (LLMs) offer significant potential for intelligent question answering (Q&A) in healthcare, yet traditional knowledge representation methods fail to capture the complex, hierarchical nature of Traditional Chinese Medicine (TCM) knowledge systems. The lack of effective retrieval-augmented generation (RAG) frameworks specifically tailored for TCM’s unique epistemology limits applications.ObjectivesThis study aims to evaluate the effectiveness of a novel Tree-Organized Self-Reflective Retrieval (TOSRR) framework in enhancing LLM performance on TCM Q&A tasks through innovative knowledge organization and dynamic self-correction mechanisms.MethodsWe developed a hierarchical knowledge representation system that structures TCM knowledge as subject-predicate-object-text (SPO-T) units within a tree-like architecture, enabling multi-dimensional relationships while preserving semantic context. Our iterative self-reflection mechanism implements dynamic knowledge retrieval and validation across textbook chapters and disciplines. Performance was evaluated using randomly selected questions from the TCM Medical Licensing Examination (MLE) and college Classics Course Exam (CCE), representing both standardized clinical knowledge and classical theory assessment.ResultsWhen integrated with GPT-4, the TOSRR framework demonstrated a 19.85% improvement in absolute accuracy on the TCM MLE benchmark and increased recall accuracy from 27 to 38% on CCE datasets. Expert manual evaluation revealed substantial enhancements across critical dimensions: safety, consistency, explainability, compliance, and coherence, with a comprehensive improvement of 18.64 points. Retrieval-Augmented Generation Assessment (RAGAs) metrics confirmed the framework’s superior knowledge utilization, retrieval precision, and resistance to information noise compared to standard RAG approaches.ConclusionThe TOSRR framework enhances LLM performance in TCM knowledge tasks through its hierarchical knowledge representation and self-reflective retrieval approach. And the framework has potential for application in teaching.

Type 2 diabetes mellitus (T2DM) and Major depressive disorder (MDD) act as risk factors for each other, and the comorbidity of both significantly increases the all-cause mortality rate. Therefore, studying the diagnosis and treatment of diabetes with depression (DD) is of great significance. In this study, we progressively identified hub genes associated with T2DM and depression through WGCNA analysis, PPI networks, and machine learning, and constructed ROC and nomogram to assess their diagnostic efficacy. Additionally, we validated these genes using qRT-PCR in the hippocampus of DD model mice. The results indicate that UBTD1, ANKRD9, CNN2, AKT1, and CAPZA2 are shared hub genes associated with diabetes and depression, with ANKRD9, CNN2 and UBTD1 demonstrating favorable diagnostic predictive efficacy. In the DD model, UBTD1 (p > 0.05) and ANKRD9 (p < 0.01) were downregulated, while CNN2 (p < 0.001), AKT1 (p < 0.05), and CAPZA2 (p < 0.01) were upregulated. We have discussed their mechanisms of action in the pathogenesis and therapy of DD, suggesting their therapeutic potential, and propose that these genes may serve as prospective diagnostic candidates for DD. In conclusion, this work offers new insights for future research on DD.

Objectives: Large language models (LLMs) can harness medical knowledge for intelligent question answering (Q A), promising support for auxiliary diagnosis and medical talent cultivation. However, there is a deficiency of highly efficient retrieval-augmented generation (RAG) frameworks within the domain of Traditional Chinese Medicine (TCM). Our purpose is to observe the effect of the Tree-Organized Self-Reflective Retrieval (TOSRR) framework on LLMs in TCM Q A tasks. Materials and Methods: We introduce the novel approach of knowledge organization, constructing a tree structure knowledge base with hierarchy. At inference time, our self-reflection framework retrieves from this knowledge base, integrating information across chapters. Questions from the TCM Medical Licensing Examination (MLE) and the college Classics Course Exam (CCE) were randomly selected as benchmark datasets. Results: By coupling with GPT-4, the framework can improve the best performance on the TCM MLE benchmark by 19.85 improve recall accuracy from 27 the framework improves a total of 18.52 points across dimensions of safety, consistency, explainability, compliance, and coherence. Conclusion: The TOSRR framework can effectively improve LLM's capability in Q A tasks of TCM.

ETHNOPHARMACOLOGICAL SIGNIFICANCE:Huanglian Zhimu decoction (HLZMD), a classical formulation in traditional Chinese medicine, has historically been utilized in the management of diabetes. However, its therapeutic efficacy and the underlying mechanisms in the context of T2DM, particularly in relation to hepatic lipid dysregulation, have yet to be systematically investigated. AIM OF THE STUDY:To explore the potential therapeutic effects and molecular mechanisms of HLZMD on T2DM. MATERIALS AND METHODS:Initially, a T2DM model was established in spontaneously diabetic Goto-Kakizaki (GK) rats through high-fat diet induction. To elucidate the molecular mechanisms underlying the therapeutic effects of HLZMD, an integrative approach combining hepatic lipidomic profiling and transcriptomic sequencing was employed to identify HLZMD-responsive pathways. Furthermore, the expression levels of key proteins within the PDE4D/cAMP/PKA signaling pathway were quantified via western blotting in both rat liver tissues and palmitic acid-stimulated HepG2 cells. To validate the pathway specificity, pharmacological inhibition experiments were performed using roflumilast, a selective PDE4D antagonist. Lastly, the chemical composition of HLZMD was characterized through ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS), and molecular docking analysis was conducted to predict potential active components interacting with PDE4D. RESULTS:In vivo experiments demonstrated that HLZMD significantly ameliorated fasting blood glucose levels and hepatic steatosis in T2DM rats. Lipidomic analysis further revealed that HLZMD effectively restored the homeostasis of diacylglycerols (DG), triglycerides (TG), sterols (ST), sphingolipids (SP), and glycerophospholipids (GP) in the liver. Integrative analyses incorporating lipidomics, transcriptomics, and western blotting suggested that HLZMD-mediated hepatic lipid modulation may be attributed to the regulation of the PDE4D/cAMP/PKA signaling pathway. In vitro, HLZMD treatment resulted in a significant reduction in extracellular glucose concentrations as well as intracellular TC and TG levels. Concurrently, HLZMD markedly upregulated the expression of PDE4D, SIRT1, and PPARγ proteins while downregulating the expression of cAMP, phosphorylated PKA (p-PKA/PKA), and phosphorylated hormone-sensitive lipase (p-HSL/HSL). Notably, pharmacological inhibition with roflumilast, a selective PDE4D antagonist, partially reversed the HLZMD-induced reduction in lipid deposition, supporting the specificity of this pathway in mediating HLZMD's effects. Furthermore, UPLC-Q-TOF-MS identified 80 chemical constituents in HLZMD. Molecular docking analysis predicted that 21 of these compounds may exhibit direct binding affinity for PDE4D, potentially modulating the cAMP/PKA signaling cascade. CONCLUSION:This study is the first to provide evidence that HLZMD exerts its pharmacological effects through multi-component interactions with PDE4D, thereby modulating the cAMP/PKA signaling pathway. This regulatory mechanism contributes to the reduction of hepatic lipid accumulation, attenuation of hepatic insulin resistance, and restoration of glucose and lipid metabolic homeostasis.

ObjectiveTo identify the risk factors that influence the time in range (TIR) of blood glucose during hospitalization in patients with type 2 diabetes mellitus (T2DM) undergoing short-term intensive insulin therapy (SIIT), and to establish a predictive model for in-hospital blood glucose fluctuations based on real-world data.MethodsRetrospective data of T2DM patients who were admitted to the Second Affiliated Hospital of Zhejiang Chinese Medicine University for SIIT between 2017 and March 2024 were collected. Random allocation was used to divide the dataset into a training set and a validation set at a ratio of 7:3. Prediction models were constructed separately using logistic regression and random forest algorithms. Additionally, a nomogram was developed for facilitating clinical application.ResultsA total of 796 T2DM patients who received SIIT were included, with 651 achieving TIR ≥ 70% within 10 days of hospitalization. Increasing age, fasting blood glucose (FBG), and use of glinides had a negative effect on achieving TIR ≥ 70%. In contrast, female sex and higher lymphocyte count were associated with increased likelihood of achieving TIR ≥ 70%. In the subgroup analysis, FBG, the presence of diabetic nephropathy (DN), and the occurrence of major adverse cardiovascular events (MACE) were found to potentially reduce the risk of achieving both TIR ≥ 70% and TITR ≥ 50% within 10 days of hospitalization. For model performance evaluation, the logistic regression model demonstrated slightly superior predictive accuracy (F1 score = 0.89, AUC = 0.80) compared with the random forest model (F1 score = 0.84, AUC = 0.72) on the full sample. After applying undersampling, the model’s ability to correctly identify negative cases improved, with specificity increasing to 0.53.ConclusionThis study, based on real-world data, developed a machine learning model (including logistic regression and random forest) to predict the achievement of TIR during hospitalization. The model not only identifies key clinical factors influencing blood glucose fluctuations, but also provides quantifiable decision support for personalized glucose management. This model has the potential to offer new insights and methods for early identification of high-risk patients and optimization of SIIT treatment strategies in clinical practice.

BACKGROUND:The uric acid to high-density lipoprotein cholesterol ratio (UHR) has emerged as a novel metabolic marker and is proven to be associated with diabetes risk. However, there is still a lack of systematic research regarding its role in gender differences and underlying mechanisms. This study aims to assess the association of UHR with diabetes risk in the context of gender differences and to investigate its mediation effects through metabolic and inflammatory pathways. METHODS:This study utilized data from NHANES 2005-2010 and included 6,843 adult participants. Multivariate logistic regression was employed to assess the association between UHR and diabetes risk, and restricted cubic spline (RCS) along with correlation analysis was applied to explore its relationship with metabolic risk factors. Multiple mediation analysis was conducted to evaluate the mediating effects of homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides (TG), and C-reactive protein (CRP) on the association between UHR and diabetes risk. RESULTS:In the overall population, UHR was significantly positively associated with diabetes risk, but gender-stratified analysis revealed a stronger predictive effect in women. In the unadjusted model, every unit increase in UHR was linked to an 18.6% increase in diabetes risk in women (p < 0.001). In the quartile analysis, women in the highest quartile showed an 8.49-fold increased risk of diabetes (OR = 8.494, 95% CI: 5.542-13.019, p < 0.001), whereas no significant association was observed in men (p > 0.05). Mediation analysis revealed that HOMA-IR was the main mediator of the relationship between UHR and diabetes risk, with mediation effects of 64.55%, 118.38%, and 39.09% in the overall population, men, and women, respectively. Additionally, the mediation effect of TG was stronger in men (36.78%) and weaker in women (17.31%). The mediation effect of CRP was relatively minimal across all groups, accounting for 7.62% in men and 2.67% in women. CONCLUSION:This study demonstrates that the association between UHR and diabetes risk exhibits gender differences, with higher diabetes risk observed in women, while men show stronger mediation effects in insulin resistance, lipid metabolism, and inflammatory response.

BackgroundKnowledge graphs (KGs) can integrate domain knowledge into a traditional Chinese medicine (TCM) intelligent syndrome differentiation model. However, the quality of current KGs in the TCM domain varies greatly, related to the lack of knowledge graph completion (KGC) and evaluation methods. ObjectiveThis study aims to investigate KGC and evaluation methods tailored for TCM domain knowledge. MethodsIn the KGC phase, according to the characteristics of TCM domain knowledge, we proposed a 3-step “entity-ontology-path” completion approach. This approach uses path reasoning, ontology rule reasoning, and association rules. In the KGC quality evaluation phase, we proposed a 3-dimensional evaluation framework that encompasses completeness, accuracy, and usability, using quantitative metrics such as complex network analysis, ontology reasoning, and graph representation. Furthermore, we compared the impact of different graph representation models on KG usability. ResultsIn the KGC phase, 52, 107, 27, and 479 triples were added by outlier analysis, rule-based reasoning, association rules, and path-based reasoning, respectively. In addition, rule-based reasoning identified 14 contradictory triples. In the KGC quality evaluation phase, in terms of completeness, KG had higher density and lower sparsity after completion, and there were no contradictory rules within the KG. In terms of accuracy, KG after completion was more consistent with prior knowledge. In terms of usability, the mean reciprocal ranking, mean rank, and hit rate of the first N tail entities predicted by the model (Hits@N) of the TransE, RotatE, DistMult, and ComplEx graph representation models all showed improvement after KGC. Among them, the RotatE model achieved the best representation. ConclusionsThe 3-step completion approach can effectively improve the completeness, accuracy, and availability of KGs, and the 3-dimensional evaluation framework can be used for comprehensive KGC evaluation. In the TCM field, the RotatE model performed better at KG representation.

Background: Knowledge graphs (KGs) can integrate domain knowledge into a traditional Chinese medicine (TCM) intelligentsyndrome differentiation model. However, the quality of current KGs in the TCM domain varies greatly, related to the lack ofknowledge graph completion (KGC) and evaluation methods.Objective: This study aims to investigate KGC and evaluation methods tailored for TCM domain knowledge.Methods: In the KGC phase, according to the characteristics of TCM domain knowledge, we proposed a 3-step"entity-ontology-path" completion approach. This approach uses path reasoning, ontology rule reasoning, and association rules.In the KGC quality evaluation phase, we proposed a 3-dimensional evaluation framework that encompasses completeness,accuracy, and usability, using quantitative metrics such as complex network analysis, ontology reasoning, and graph representation.Furthermore, we compared the impact of different graph representation models on KG usability.Results: In the KGC phase, 52, 107, 27, and 479 triples were added by outlier analysis, rule-based reasoning, association rules,and path-based reasoning, respectively. In addition, rule-based reasoning identified 14 contradictory triples. In the KGC qualityevaluation phase, in terms of completeness, KG had higher density and lower sparsity after completion, and there were nocontradictory rules within the KG. In terms of accuracy, KG after completion was more consistent with prior knowledge. In termsof usability, the mean reciprocal ranking, mean rank, and hit rate of the first N tail entities predicted by the model (Hits@N) ofthe TransE, RotatE, DistMult, and ComplEx graph representation models all showed improvement after KGC. Among them, theRotatE model achieved the best representation.Conclusions: The 3-step completion approach can effectively improve the completeness, accuracy, and availability of KGs,and the 3-dimensional evaluation framework can be used for comprehensive KGC evaluation. In the TCM field, the RotatE modelperformed better at KG representatio

Diabetes is a significant global health issue, causing extensive morbidity and mortality, and represents a serious threat to human health. Recently, the bioactive lipid molecule Sphingosine-1-Phosphate has garnered considerable attention in the field of diabetes research. The aim of this study is to comprehensively understand the mechanisms by which Sphingosine-1-Phosphate regulates diabetes. Through comprehensive bibliometric analysis and an in-depth review of relevant studies, we investigated and summarized various mechanisms through which Sphingosine-1-Phosphate acts in prediabetes, type 1 diabetes, type 2 diabetes, and their complications (such as diabetic nephropathy, retinopathy, cardiovascular disease, neuropathy, etc.), including but not limited to regulating lipid metabolism, insulin sensitivity, and inflammatory responses. This scholarly work not only unveils new possibilities for using Sphingosine-1-Phosphate in diabetes treatment but also offers fresh insights and recommendations for future research directions to researchers.

The success of large language models (LLMs) in general areas have sparked a wave of research into their applications in the medical field. However, enhancing the medical professionalism of these models remains a major challenge. This study proposed a novel model training theoretical framework, the M-KAT framework, which integrated domain-specific training methods for LLMs with the unique characteristics of the medical discipline. This framework aimed to improve the medical professionalism of the models from three perspectives: general knowledge acquisition, specialized skill development, and alignment with clinical thinking. This study summarized the outcomes of medical LLMs across four tasks: clinical diagnosis and treatment, medical question answering, medical research, and health management. Using the M-KAT framework, we analyzed the contribution to enhancement of professionalism of models through different training stages. At the same time, for some of the potential risks associated with medical LLMs, targeted solutions can be achieved through pre-training, SFT, and model alignment based on cultivated professional capabilities. Additionally, this study identified main directions for future research on medical LLMs: advancing professional evaluation datasets and metrics tailored to the needs of medical tasks, conducting in-depth studies on medical multimodal large language models (MLLMs) capable of integrating diverse data types, and exploring the forms of medical agents and multi-agent frameworks that can interact with real healthcare environments and support clinical decision-making. It is hoped that predictions of work can provide a reference for subsequent research.

Depression impairs self-management in diabetic patients, exacerbates insulin resistance, and elevates glycated hemoglobin (HbA1c) levels, thereby increasing diabetes risk. This study analyzed data from 30,386 participants in the National Health and Nutrition Examination Survey (NHANES), assessing depression severity using the 9-item Patient Health Questionnaire (PHQ-9) and evaluating diabetes status through clinical markers such as HbA1c, random blood glucose, and fasting blood glucose. Participants were stratified by depression severity and diabetes status to examine the relationship between depression and diabetes risk. We applied descriptive statistics, logistic regression models, subgroup analyses, and restricted cubic spline (RCS) modeling to explore this association. The results revealed that greater depression severity was significantly associated with increased diabetes incidence, elevated HbA1c, fasting glucose, and insulin levels. Multivariate regression analysis confirmed a consistent positive correlation between depression severity and diabetes risk. Subgroup analyses further identified significant relationships between depression and various demographic and behavioral factors, including gender, race, BMI, smoking status, and prediabetic conditions. Additionally, the RCS model demonstrated a clear increase in diabetes risk with rising PHQ-9 scores. In conclusion, our study demonstrates that the severity of depression is positively correlated with the risk of diabetes, and this association may be closely linked to various glycemic and lipid metabolic parameters.

PurposeThe aim is to provide new insights for researchers studying the pathogenesis of diabetic cognitive dysfunction and promoting the wider use of natural products in their treatment.MethodFirst, the Web of Science Core Collection was selected as the data source for a computerized literature search on oxidative stress and diabetic cognitive dysfunction (DCD). Next, Biblimetrix and VOSviewer performed statistical analysis focusing on publication countries, institutions, authors, research hotspots, and emerging directions in the field. Then, through the analysis of keywords and key articles, the forefront of the field is identified. Finally, we discussed the pathogenesis of DCD, the influence of oxidative stress on DCD and the antioxidant effect of natural products on DCD.Result293 valid papers were obtained. Bibliometrics showed that oxidative stress, diabetes, Alzheimer’s disease (AD), cognitive decline, insulin resistance and quercetin were the key words of the symbiotic network.ConclusionThe antioxidant effects of natural products in improving DCD have been extensively studied in preclinical studies, providing potential for their treatment in DCD, but their evaluation in clinical trials is currently uncommon.

Background Medicinal and food homologous plants (MFHPs) which can improve Type 2 Diabetes Mellitus (T2DM) draw significant attention among the public due to their low toxicity and more safety. Polysaccharides, one of the various active components of MFHPs, are recognized as effective modulators of the intestinal flora. By altering the composition of intestinal flora and affecting their metabolic products, polysaccharides can improve T2DM, making them a central focus of anti-diabetic research. Purpose The purpose of this study is to systematically review the mechanism by which polysaccharides from MFHPs (MFHPPs) regulate the composition of intestinal flora and its metabolic products to improve T2DM. Methods This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and conducts a comprehensive search on the PubMed, Web of Science and Embase databases. All experimental articles published up to March 4, 2024, are included in the search. Results Among the 5,733 articles reviewed, 29 were selected, covering 22 different MFHPs. MFHPPs can improve T2DM, particularly in lowering blood glucose levels, with consistent results. MFHPPs can regulate the diversity of intestinal flora in T2DM animal models, primarily affecting four phyla: decreasing Firmicutes and Proteobacteria while increasing Bacteroidetes and Actinobacteriota. At the genus level, the improvement of T2DM by MFHPPs is associated with the modulation of 12 key genera: Allobaculum, Akkermansia, Bifidobacterium, Lactobacillus, Helicobacter, Halomonas, Olsenella, Oscillospira, Shigella, Escherichia-Shigella, Romboutsia and Bacteroides. At the molecular level, MFHPPs primarily act by modulating the intestinal flora to increase short-chain fatty acid levels, promote the secretion of glucagon-like peptide-1, influence the IGF1/PI3K/AKT signaling pathway, or the PI3K/AKT/GSK-3β pathway, to lower blood glucose levels. They may also improve T2DM by working in glucose metabolism through the "microbiota-gut-organ" axis. MFHPPs can also alleviate T2DM by mitigating inflammation and oxidative stress: MFHPPs regulate intestinal flora to reduce lipopolysaccharide "leakage" and enhance intestinal mucosal permeability to tackle the inflammation associated with T2DM; MFHPPs enhance the expression of oxidative stress-related enzymes to alleviate oxidative stress and improve T2DM. Lastly, from a metabolic pathway perspective, MFHPPs are primarily involved in the metabolism of amino acids and their derivatives, carbohydrate metabolism and glutathione metabolism. Conclusion MFHPPs can improve T2DM by enhancing the composition of intestinal flora, regulating its metabolic products to promote insulin secretion, inhibiting glucagon-like peptide secretion, facilitating glycogen synthesis, reducing inflammation levels and alleviating oxidative stress. Furthermore, MFHPPs demonstrate potential protective effects on critical organs such as the pancreas, liver, kidneys and heart. Therefore, MFHPPs demonstrate significant clinical potential. However, most studies can only indicate the potential of MFHPPs intervention in improving T2DM through the intestinal flora. The causality between MFHPPs regulating the intestinal flora and T2DM requires further investigation.

The global prevalence of type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) is rapidly increasing, revealing a strong association between these two diseases. Currently, there are no curative medication available for the comorbidity of T2DM and AD. Ceramides are structural components of cell membrane lipids and act as signal molecules regulating cell homeostasis. Their synthesis and degradation play crucial roles in maintaining metabolic balance in vivo, serving as important mediators in the development of neurodegenerative and metabolic disorders. Abnormal ceramide metabolism disrupts intracellular signaling, induces oxidative stress, activates inflammatory factors, and impacts glucose and lipid homeostasis in metabolism-related tissues like the liver, skeletal muscle, and adipose tissue, driving the occurrence and progression of T2DM. The connection between changes in ceramide levels in the brain, amyloid beta accumulation, and tau hyper-phosphorylation is evident. Additionally, ceramide regulates cell survival and apoptosis through related signaling pathways, actively participating in the occurrence and progression of AD. Regulatory enzymes, their metabolites, and signaling pathways impact core pathological molecular mechanisms shared by T2DM and AD, such as insulin resistance and inflammatory response. Consequently, regulating ceramide metabolism may become a potential therapeutic target and intervention for the comorbidity of T2DM and AD. The paper comprehensively summarizes and discusses the role of ceramide and its metabolites in the pathogenesis of T2DM and AD, as well as the latest progress in the treatment of T2DM with AD.

分析了经方理论与图论的相关性,提出基于图论的经方人工智能(AI)研究路径,从逻辑推理和命题逻辑角度分析图论与经方理论、思维的相关性,提出可行的研究路径.图论的应用能解决经方智能化研究中的知识表示问题,图的属性和度量方法有助于从中医思维出发进行知识发现,图的矩阵表示和图连通性能为经方智能辅助诊疗模型融入领域知识,提升模型效率.图论可为经方AI研究提供理论指导,基于图论的知识图谱等研究技术可为经方AI研究中的难题提供解决方案.

文章通过梳理经方古籍与经典文献,结合临床特点,总结了2型糖尿病"脾瘅期-黄汗期-消瘅期-消渴期"4个发展阶段的病因病机及其所对应的"阳明病-太阴病-太阴阳明合病-厥阴病"六经病传规律,并结合经方理论总结出各阶段的常用方药.脾瘅期病在阳明,治以黄连类方、栀子豉类方、白虎类方为主;黄汗期病在太阴,治以黄芪类方为主;消瘅期病在太阴阳明,治以近效消渴方和黄连知母丸为主;消渴期病在厥阴,治以乌梅类方、柴胡类方为主.

以《黄帝内经》"天人相应"理论为基础的中医时间观揭示了时间节律对人体的影响.《金匮要略》将时间医学融入杂病辨证及治疗中,根据疾病发生发展所具有的典型时间节律,进行诊断治疗,以达到判断病势走向、提高药效、防止传变的目的.文章将《金匮要略》记载的时间节律进行整理归纳,旁参《黄帝内经》理论,从按时而生、因时致病、顺时诊断、择时治病、望时预观五方面进行阐释,有助于内伤杂病全过程的诊治,具有重要的临床价值.

Ethnopharmacological relevance: Huanglian Zhimu Decoction (HLZMD) is a Chinese medicine used to treat type 2 diabetes mellitus(T2DM) since the ancient Tang Dynasty, but the mechanism is still unknown.Aim of the study: To study the effects and mechanisms of HLZMD in T2DM rats.Materials and methods: The rats were fed a customized high-fat diet and naturally generated T2DM model. HLZMD was used to treat the T2DM rats for 12 weeks. The body weight, food and water intake, urine output, and fasting blood glucose of rats were measured weekly. Automated biochemistry instruments detected blood lipids, such as cholesterol (CHOL), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and triglycerides (TG). The steatosis and inflammatory status of the liver and colon were studied using HE (hematoxylin-eosin) staining. 16S rDNA gene sequences analysis and fecal metabolomics analysis were used. Farnesoid X receptor (FXR) and fibroblast growth factor 15(FGF15) were examined by real-time reverse transcription-quantitative polymerase chain reaction. The main constituents in the HLZMD aqueous extract were characterized by a UPLC-Q-TOF-MS.Results: In this study conducted on T2DM rats, the effectiveness of HLZMD in alleviating symptoms and reducing blood glucose levels was validated. HLZMD was found to decrease CHOL, HDL, and LDL levels. HLZMD attenuated liver steatosis and inflammation in T2DM rats fed a high-fat diet. It also partially restored intestinal barrier function and mitigated colon tissue inflammation. Fecal metabolomics analysis revealed that HLZMD affected bile acid and linoleic acid. The 16S rDNA gene sequencing analysis demonstrated that HLZMD restored the dysbiosis of intestinal microbiota at both phylum and genus levels, including Firmicutes, Bacteroides, Akkermansia, Allobaculum, Colidextribacter, Oscillibacter, Dorea and Prevotella_9. These regulated floras exhibited significant correlations with bile acid metabolism, linoleic acid metabolism, and blood lipid levels. HLZMD also acted on the FXR and FGF15 signaling pathways.Conclusions: This study identified the active components and relevant mechanisms of HLZMD in treating T2DM. Our findings predicted that HLZMD balances gut microbiota and modulates gut metabolite, leading to improved blood lipids and glucose levels by activating the FXR and FGF15 signaling pathways.

Modern research and clinical practice have proved that Traditional Chinese Medicine (TCM) has unique advantages in the treatment of diabetes. JinXiaoXiaoKe decoction (JXXKD) is a prescription for treating diabetes used in ancient China and still has a good clinical effect today. However, the mechanism of JXXKD against T2DM is unclear. The purpose of this study is to screen the targets through network pharmacology, and to explore the therapeutic effect and mechanism of JXXKD on diabetic rats. The JXXKD's active components, related targets and T2DM targets were obtained from the public database, and Venny 2.1 was used to determine the common targets of JXXKD in the treatment of T2DM, and PPI, GO and KEGG analysis were performed. The core components and targets are verified by molecular docking. The diabetic Goto–Kakizaki rat model was established by high-sugar and high-fat diet, Wistar rats were used as a blank control group. The diabetic rats were randomized into three groups and administered saline (MO), metformin (MET), or JXXKD once a day for 4 weeks. Fasting blood glucose (FBG), Fasting serum insulin (FINS), and HOMA-IR were detected, hematoxylin-eosin (HE) staining was used to observe the pathological changes of liver tissue; and qPCR was used to detect the expression of relevant genes screened by network pharmacology. 93 active components and 296 targets of JXXKD were identified, of which 156 overlapped with T2DM-related targets. PPI network showed that APP, AKT1, ANXA1, RXRA, C3, EGFR, ESR1, RELA, IL6, and MAPK8 were the top 10 relevant targets. GO analysis showed the common targets are mainly involved in oxidative stress, lipid metabolism, and nutrient levels, while KEGG analysis showed these targets may regulate lipids and atherosclerosis, AGE-RAGE signaling pathway, and TNF signaling pathway. Molecular docking suggested a satisfactory potential for key components to bind to these significant targets. The animal experiments showed that JXXKD significantly improved the symptoms of polyuria, decreased the protein levels of FBG and HOMA-IR, improved liver fat deposition, and decreased the gene expressions of Foxo1, Pparg and Akt in diabetic rats. The mechanism of JXXKD treat T2DM may be achieved by modulating the expression of FOXO1, PPARG, and AKT, regulating the glucose and lipids metabolism.