Introduction

Obesity and its associated insulin resistance (IR) represent a critical global public health challenge.1 This metabolic dysregulation is not only a core precursor to type 2 diabetes mellitus (T2DM), but also a trigger for conditions such as non-alcoholic fatty liver disease, cardiovascular diseases, and metabolic syndrome.2 As a result, patient mortality rises considerably, placing a substantial strain on healthcare systems worldwide.3 The underlying pathophysiology is complex and multifactorial, involving genetic predisposition, dietary excess, physical inactivity, and adipose tissue dysfunction among other contributing factors.4 Among these, chronic low-grade inflammation arising from impaired adipose tissue function has been recognized as one of the central drivers of metabolic deterioration.5 Excessive fat accumulation leads to adipocyte hypertrophy and apoptosis, releasing free fatty acids and damage-associated molecular patterns (DAMPs).6 This recruits immune cells such as macrophages, activating innate immune signaling pathways and creating a vicious cycle of fat accumulation, inflammatory activation, and IR.7

In this inflammatory network, the Toll-like receptor 4 (TLR4)/NOD-like receptor family pyrin domain containing 3 (NLRP3) signaling axis has emerged as one of the key pathways driving metabolic inflammation.8 TLR4 recognizes ligands such as lipopolysaccharide (LPS) or endogenous DAMPs including high mobility group box 1 (HMGB1),9,10 and upon activation initiates the myeloid differentiation primary response 88 (MyD88)-dependent pathway, upregulating pro-inflammatory cytokines including tumor necrosis factor (TNF)-α and interleukin (IL)-6.11,12 Concurrently, TLR4 activation induces NLRP3 inflammasome assembly, leading to caspase-1 activation and IL-1β secretion.13,14 These cytokines directly interfere with insulin signaling and reduce insulin sensitivity in the liver, muscle, and adipose tissue,15 and this IR is thought to further amplify inflammatory responses, potentially forming a self-perpetuating pathological loop.16 Recent studies have corroborated this pathogenic axis, showing that pharmacological inhibition of TLR4 or NLRP3 reduces adipose inflammation and improves insulin sensitivity in obese models, highlighting the therapeutic potential of targeting this pathway.17,18

Given the multifaceted pathophysiology of obesity-associated IR, current clinical interventions remain limited in their ability to comprehensively address these interconnected abnormalities. Existing strategies primarily include lifestyle modification and glucose-lowering agents such as metformin and glucagon-like peptide-1 receptor agonists.19 However, these approaches are often hampered by poor patient adherence, suboptimal long-term efficacy, and potential side effects including gastrointestinal (GI) disturbances or cardiovascular risks.20 Consequently, identifying safe and effective anti-inflammatory and insulin-sensitizing agents from natural products has become a major research focus in the field of metabolic diseases.

Polygonati Odorati Rhizoma (Yuzhu), the dried rhizome of Polygonatum odoratum (Mill). Druce (Liliaceae),21 has been historically used as a traditional Chinese medicinal and edible herb for its tonifying and heat-clearing properties.22 In modern clinical practice, it is frequently incorporated into formulations for managing metabolic disorders such as diabetes and hyperlipidemia.23,24 A growing body of evidence supports its beneficial metabolic effects: for instance, Polygonati odoratum polysaccharides have been shown to lower fasting blood glucose and improve oral glucose tolerance in streptozotocin-induced diabetic mice,25,26 while its flavonoid-rich fractions can reduce serum triglycerides and total cholesterol in high-fat diet-fed animals.23 Moreover, extracts of Yuzhu have been reported to attenuate oxidative stress by enhancing superoxide dismutase and glutathione peroxidase activities, and to suppress inflammatory responses through downregulation of TNF-α and IL-6 in various experimental models.27,28 Several classes of bioactive constituents, including polysaccharides, flavonoids, steroidal saponins, and phenolic compounds, are proposed to contribute to these pharmacological activities.29,30

Despite these promising observations, key gaps remain. The specific bioactive components responsible for Yuzhu’s anti-obesity and insulin-sensitizing effects have not been definitively identified, and the molecular mechanisms underlying these actions, particularly the potential involvement of the TLR4/NLRP3 pathway, remain largely unexplored. To address these gaps, the present study employed an integrated strategy combining ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UHPLC-Q-TOF-MS/MS) chemical profiling, network pharmacology, molecular docking, and experimental validation to systematically identify the active constituents of Yuzhu and to investigate whether and how its extract alleviates obesity-associated IR, with particular attention to the TLR4/NLRP3 signaling axis.

MethodsPreparation of Yuzhu Extract

Polygonati Odorati Rhizoma was sourced from the Shaoyang Academy of Agricultural Sciences (Batch No.: 2025–10-001) and authenticated by Dr. Xinghui Wang. Dried rhizomes were cleaned, fibrous roots were removed, and the material was chopped. Extraction was performed using 30% ethanol as the solvent. The extract was filtered through a G4 funnel, and ethanol was recovered using a rotary evaporator. The resulting yellowish-brown residue was dried in an oven to obtain the extract. Before experiments, the extract was dissolved in appropriate solvents to prepare solutions of varying concentrations as needed.31

UHPLC-Q-TOF-MS/MS Analysis of Yuzhu Extract

The chemical composition of the Yuzhu extract was analyzed by UHPLC-Q-TOF-MS/MS (Agilent 6545). Separation was performed on a C18 reversed-phase column (2.1 × 100 mm, 1.8 μm) at 40°C, using a mobile phase consisting of methanol (A) and 0.1% aqueous formic acid (B). A gradient elution was applied as follows: 5% A (0–2 min), 5–95% A (2–15 min), 95% A (15–18 min), and 5% A (18–20 min), with a flow rate of 0.3 mL/min. After filtration through a 0.22-μm membrane, 2 μL of the sample was injected. MS data were collected in both positive and negative ionization modes under the following conditions: electrospray ionization voltage, 3.5 kV (positive) and −3.0 kV (negative); capillary temperature, 325°C; gas flow, 10 L/min; fragmentor energy, 20–40 eV. Compounds were identified by matching the acquired data against standard compound databases.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Analysis

GO functional enrichment and KEGG pathway analyses were performed on the primary targets of Yuzhu via the Database for Annotation, Visualization and Integrated Discovery (DAVID; https://davidbioinformatics.nih.gov/tools.jsp, accessed on 29 October 2025). For visualization, the top 5 GO terms and the top 20 enriched KEGG pathways were subsequently plotted on a bioinformatics platform (http://www.bioinformatics.com.cn/) to elucidate signaling pathways associated with key molecular biological processes and pivotal targets.

Molecular Docking Validation

To validate the interactions, the top five core compounds with the highest degree values in the herb-active ingredient-target-disease network were selected for molecular docking. The two-dimensional structures of the active compounds were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/, accessed on 30 October 2025), optimized using Chem3D software, and saved in PDBQT format after preprocessing with AutoDockTools 1.5.7. The three-dimensional structures of TLR4 and NLRP3 proteins were downloaded from the Protein Data Bank (PDB, https://www.rcsb.org, accessed on 30 October 2025). These proteins were prepared in PyMOL by removing water molecules and extraneous ligands, adding hydrogen atoms, and subsequently saved as receptors in PDBQT format using AutoDockTools 1.5.7. Docking simulations were then conducted with AutoDockTools 1.5.7, and the results were visualized with PyMOL software.32

Animal Handling and Grouping

C57BL/6J mice (7 weeks old, male), obtained from Vital River (Beijing, China), were acclimatized for one week. Mice were housed under specific pathogen-free conditions (22 ± 2°C, 50–60% humidity, 12-h light/dark cycle) with free access to food and water. Subsequently, they were randomized into 5 groups (n = 8 each): control, high-fat diet (HFD), Yuzhu-L, Yuzhu-M, and Yuzhu-H. The sample size was determined based on previous studies employing similar HFD-induced obesity models.33 No animals were excluded from the analysis unless death unrelated to the experimental intervention or severe technical failure occurred. Investigators responsible for outcome assessment and data analysis were blinded to group allocation. The control group was fed a standard diet (10% kcal from fat), whereas the remaining groups received a D12492 HFD (Research Diets, New Brunswick, NJ, USA), consisting of 60% kcal from fat, 20% kcal from carbohydrate, and 20% kcal from protein, for 12 weeks to establish the model.34 After modeling, mice in the Yuzhu-treated groups were administered Yuzhu extract via oral gavage at doses of 1, 3, and 5 g/kg/day (Yuzhu-L, -M, and -H, respectively, expressed as crude drug equivalents) for 4 weeks.31 Body weight was monitored every two weeks throughout the study. Animals were monitored daily. Predefined humane endpoints included severe weight loss (> 20% of baseline body weight), inability to access food or water, persistent lethargy, or signs of severe distress. No animals reached the predefined humane endpoints during the study. At the end of the experiment, mice were humanely euthanized by intraperitoneal injection of sodium pentobarbital (150 mg/kg) prior to tissue collection. Epididymal white adipose tissue was collected for subsequent analyses. Animal experiments were conducted and reported in accordance with the ARRIVE 2.0 guidelines. All procedures were conducted in compliance with protocols approved by the Ethics Committee of Hunan Evidence-based Biotechnology Co., Ltd. (Ethics Number: XZ258376).

Biochemical Analysis

Blood was allowed to clot at room temperature for 1 h, followed by centrifugation (2000 rpm, 15 min) to obtain serum. Serum concentrations of triglycerides (TG), total cholesterol (T-CHO), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were subsequently determined utilizing an ADVIA-2400 automatic clinical chemistry analyzer (Siemens, Germany).

Measurement of Fasting Blood Glucose and Plasma Insulin

After a 6-h fast, mice were anesthetized, and blood samples were obtained from the retro-orbital venous plexus. Blood glucose concentrations were measured using Accu-Chek Aviva glucose test strips. The collected blood was centrifuged to obtain plasma, and insulin concentrations were quantified using a commercial enzyme-linked immunosorbent assay (ELISA) kit (Millipore, EZRMI-13K) according to the manufacturer’s protocol.

Glucose Tolerance Test (GTT) and Insulin Tolerance Test (ITT)

For the GTT, mice were fasted for 12 h and injected intraperitoneally with glucose (0.75 g/kg), and blood glucose levels were measured from tail vein samples at 0, 15, 30, 60, and 120 min. For the ITT, mice were fasted for 2 h and injected intraperitoneally with insulin (HumulinR, 0.75 U/kg), and blood glucose was monitored at the same time points.

Hematoxylin and Eosin (HE) Staining

Adipose tissues were collected, fixed, paraffin-embedded, and sectioned at approximately 4 μm thickness. The sections were deparaffinized in xylene I and II (Sigma, USA; 10 min each), dehydrated with a graded ethanol series (5 min per step), and rinsed with distilled water. Staining was carried out with hematoxylin; after rinsing under running water, the sections were counterstained with eosin. The sections were then dehydrated again, cleared in xylene, and finally mounted in neutral resin. Morphological and pathological alterations in adipose tissue were examined and imaged via an optical microscope (CX43, Olympus, Tokyo, Japan).

Immunohistochemistry (IHC)

Tissue sections were first floated in warm water (40°C). After deparaffinization with xylene and ethanol, sections were rinsed. Endogenous peroxidase activity was quenched by incubation with 3% H2O2 for 10 min. Antigen retrieval was performed by microwave heating in citrate buffer for 3 min (cooled to room temperature, repeated once). After cooling, sections were blocked and subjected to overnight incubation (4°C) with an anti-NLRP3 primary antibody (1:50, PA5-79740, Thermo Fisher, Massachusetts, USA). The next day, after washing, the sections were incubated for 1 h with a horseradish peroxidase (HRP)-conjugated goat anti-rabbit secondary antibody (1:2000, ab6702, Abcam, Cambridge, UK). Color was developed using a 3,3’-diaminobenzidine solution (P0203, Beyotime, Shanghai, China), followed by hematoxylin counterstaining. Staining results were observed using an optical microscope (CX43, Olympus). Positive cells were counted with ImageJ (V1.8.0.112, NIH, Madison, WI, USA), and the percentage of positive cells was calculated (positive cells/total cells × 100%).

ELISA

Adipose tissue samples were homogenized, and the levels of IL-6 and IL-1β in the tissue homogenates were determined using ELISA kits (98027ES48, Yeasen, Shanghai, China; D721017, Sangon, Shanghai, China, respectively) according to the manufacturers’ instructions. For cell experiments, culture supernatants were collected after the indicated treatments, and the concentrations of IL-6 and IL-1β were measured using the same ELISA kits according to the manufacturers’ protocols.

Reverse Transcription-Quantitative Polymerase Chain Reaction (RT-qPCR) Assay

Total RNA was extracted from tissues and cells using a specified kit (DP419, Jiachu Biotechnology, Shanghai, China). RT-qPCR was performed using a fluorescence quantitative kit (QR0100, Sigma-Aldrich, St. Louis, USA). Actb was employed as an internal reference. The 2-ΔΔCt method was applied to quantify relative mRNA expression, and the primer sequences used are listed in Table 1.

Table 1 Primer Sequences

Western Blot (WB) Analysis

Protein extraction was performed using radioimmunoprecipitation assay lysis buffer (ab170197, Abcam), and concentrations were measured with a bicinchoninic acid protein assay kit (P0010, Beyotime). Following separation by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, proteins were wet-transferred onto polyvinylidene difluoride membranes (ab133411, Abcam). After blocking with 5% skimmed milk for 1 h, the membranes were probed overnight (4°C) with primary antibodies: phosphorylated p65 (p-p65; 1:1000, PA5-37718, Thermo Fisher), p65 (1:5000, A19653, Abclonal), phosphorylated signal transducer and activator of transcription 3 (p-STAT3; 1:1000, 9134, Cell Signaling Technology, Shanghai, China), STAT3 (1:2000, 10253-2-AP, Proteintech), cleaved caspase-1 (1:1000, AF4005, Affinity Biosciences), and β-actin (1:80,000, AC026, Abclonal). Subsequently, a goat anti-rabbit secondary antibody (HRP-conjugated, 1:2000, ab205718, Abcam) was applied for 2 h. Detection was performed using an enhanced chemiluminescence reagent (A38554, Thermo Fisher Scientific). Densitometric analysis was conducted using ImageJ software, and the intensity of each target protein band was normalized to the corresponding β-actin band. The resulting normalized values were used for statistical analysis.

Cell Culture and Treatment

Mouse 3T3-L1 preadipocytes were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA) and cultured at 37°C with 5% CO2 in high-glucose Dulbecco’s modified Eagle medium (DMEM; Gibco, USA) supplemented with 10% fetal bovine serum (FBS; Gibco, USA) and 1% penicillin-streptomycin. Upon reaching complete confluence, adipogenic differentiation was triggered. Cells were first cultured in induction medium I ([DMEM containing 10 μg/mL insulin, 0.5 mM 3-isobutyl-1-methylxanthineIBMX, and 1 μg/mL dexamethasone (DEX)] for 24 h. The medium was then replaced with fresh basal DMEM for 24 h. Subsequently, cells were transferred to induction medium II (DMEM with 10 μg/mL insulin) for 48 h to promote lipid droplet formation. Finally, cells were maintained in regular DMEM (10% FBS), with the medium changed every 2 d. Mature adipocyte morphology (> 95% with lipid droplets) was confirmed before use. An IR state was induced by stimulating these cells with TNF-α (10 ng/mL, 24 h).35 After TNF-α stimulation, the medium was replaced with fresh culture medium, and cells were treated with the indicated concentrations of diosgenin glucoside (DG, HY-N0730) for another 24 h. In some experiments, recombinant HMGB1 (rHMGB1, 1 μg/mL, KeyGEN BioTECH Corp., Nanjing, Jiangsu)36 was added to cells along with DG for 24 h.

Oil Red O Staining

Following three washes with phosphate-buffered saline (PBS), mature adipocytes were fixed with 4% paraformaldehyde (PFA) for 30 min at room temperature and stained with oil red O for 30 min at room temperature. A working solution of oil red O was prepared by combining the stock solution and distilled water at 3:2, followed by centrifugation (250 g, 4 min), and the resulting supernatant was used for staining. Microscopic observation and image recording were performed using a microscope (Olympus).

Glucose Uptake Assay

Glucose uptake was measured using a glucose uptake assay kit (J1342, Promega, USA). After treatment, mature adipocytes were incubated with 50 μL of 1 mM 2-deoxy-D-glucose (2DG) per well at room temperature for 10 min. Subsequently, 25 μL of stop buffer, 25 μL of neutralization buffer, and 100 μL of 2DG6P detection reagent were added sequentially, followed by incubation at room temperature for 2 h. Luminescence signals were detected using a fluorescence microplate reader (TECAN, Switzerland) to assess cellular glucose uptake capacity.

Cell Counting Kit-8 (CCK-8) Assay

Differentiated adipocytes were divided into different treatment groups and treated with DG at 0, 5, 10, 20, 40, 60, 80, and 100 μg/mL. After 24 h of incubation, CCK-8 reagent (Beyotime) was added, followed by a 2-h incubation at 37°C. Absorbance (450 nm) was recorded using a microplate reader to assess the effect of DG on adipocyte viability and to determine safe and effective experimental concentrations.

Terminal Deoxynucleotidyl Transferase dUTP Nick End Labeling (TUNEL) Assay

After a single wash with PBS, cells were fixed with 4% PFA for 30 min. The fixed cells were then washed and permeabilized with a specialized solution (P0097, Beyotime) for 5 min at room temperature. TUNEL detection solution (C1089, Beyotime) and 4’,6-diamidino-2-phenylindole were then applied. Observation and imaging were performed using fluorescence microscopy, and the proportion of TUNEL-positive nuclei was calculated with ImageJ.

Data Processing

Data processing and statistical evaluation were carried out using GraphPad Prism 9. Data are presented as mean ± standard deviation (SD). Normality was assessed using the Shapiro–Wilk test, and homogeneity of variance was evaluated using Levene’s test prior to statistical analysis. Differences between two groups were analyzed using Student’s t-test, while one-way or two-way analysis of variance (ANOVA) followed by Tukey’s post hoc test was used for comparisons among three or more groups. A P value < 0.05 was considered statistically significant.

ResultsUHPLC-Q-TOF-MS/MS Profiling Revealed 127 Chemical Constituents in Yuzhu Extract

Comprehensive characterization of the Yuzhu extract by UHPLC-Q-TOF-MS/MS, using both ionization polarities, revealed a total of 127 chemical compounds after removal of duplicate identifications (Table S1). Figure 1 displays the acquired total ion chromatogram.

Figure 1 UHPLC-Q-TOF-MS/MS total ion chromatogram of the Yuzhu extract.

Network Pharmacology Identified Key Active Compounds and Therapeutic Targets of Yuzhu

Based on the 127 identified chemical constituents, Simplified Molecular-Input Line-Entry System-encoded structures were obtained from the PubChem database, and pharmacokinetic properties were evaluated using the SwissADME platform. Screening criteria were set as GI absorption “high” and a druglikeness score ≥ 3,37 resulting in 67 potential active components. These active components were categorized into amino acids and derivatives (15), phenolic acids and aldehydes (14), flavonoids (8), fatty acids and derivatives (10), coumarins (2), steroidal saponins (3), and other small-molecule metabolites (15). Potential targets for the 67 active components were predicted using the SwissTargetPrediction platform, yielding 860 unique genes after deduplication. Disease-related targets were obtained from the Gene Expression Omnibus (GEO) dataset GSE62635, which was selected because it is closely related to obesity-associated IR and inflammatory responses relevant to the present study. Gene expression data were directly downloaded from the GEO database and analyzed using the normalized expression matrix provided by the dataset. Differentially expressed genes (DEGs) were identified by comparing the control vs DEX (Figure 2A) and control vs TNF (Figure 2B) groups using the criteria of P < 0.05 and |log2FC| > 1. The integrated DEGs from both comparisons were subsequently intersected with the predicted drug targets, resulting in 23 overlapping targets (Figure 2C). Based on this, a herb-active ingredient-target-disease network was built using Cytoscape software (Figure 2D), comprising 92 nodes and 197 edges. Topological analysis was performed using the Network Analyzer plugin. The top five active components with the highest degree values were corchorifatty acid F, 9,10-dihydroxy-12Z-octadecenoic acid, N-trans-feruloyloctopamine, N-cis-feruloyloctopamine, and DG, suggesting their potential core roles in the pharmacological action of Yuzhu.

Figure 2 Network analysis of active components and potential targets of Yuzhu extract. (A) Screening of DEGs based on GEO dataset GSE62635 by comparing control vs Dex groups. (B) Screening of DEGs based on GEO dataset GSE62635 by comparing control vs TNF groups. (C) Venn diagram showing the intersection of drug targets and DEGs from the two comparisons. (D) Herb-active ingredient-target-disease network constructed using Cytoscape software.

TLR4 and CCL2 Were Identified as Core Targets Associated with NOD-Like Receptor Signaling

The screened core targets were uploaded to the DAVID database for KEGG and GO enrichment analysis. KEGG pathway analysis revealed that Yuzhu extract targets were primarily enriched in several inflammation- and signaling-related pathways, including the TNF, PI3K-Akt, NOD-like receptor, and Toll-like receptor signaling pathways (Figure 3A). GO analysis revealed that these targets were primarily involved in inflammatory response, LPS response, and GPCR signaling. They were primarily localized to the plasma membrane and cytoplasm and associated with molecular functions including nuclear receptor activity and integrin binding (Figure 3B). Subsequently, the overlapping targets were used to establish a protein-protein interaction (PPI) network (minimum interaction score: 0.4). The resulting.tsv file was imported into Cytoscape 3.7.0 for visualization (Figure 3C). This network comprised 19 nodes and 30 edges. Topological analysis was performed using the Network Analyzer plugin in Cytoscape. According to a previously published method,38 nodes with degree values greater than four times the median degree of the network were defined as hub targets. Based on this criterion, two core targets, TLR4 and CCL2, were identified for subsequent analyses. Further KEGG analysis showed that both core targets were significantly associated with the NOD-like receptor signaling pathway, suggesting its key role in the pathological process studied. TLR4, as a classic innate immune receptor, can promote NLRP3 inflammasome assembly and activation by activating downstream inflammatory signals, thereby driving inflammation mediated by the NOD-like receptor signaling pathway.

Figure 3 KEGG/GO enrichment and PPI network analysis of core targets of Yuzhu extract. (A) KEGG pathway enrichment analysis of core targets. (B) GO enrichment analysis of core targets. (C) PPI network of core targets constructed using Cytoscape software.

Yuzhu Ameliorates HFD-Induced Obesity, IR, and Adipose Inflammation

To evaluate the therapeutic effects of Yuzhu on obesity-associated metabolic disorders, an HFD-induced obese mouse model was established. Compared with the control group, HFD-fed mice exhibited significant body weight gain and elevated blood glucose levels, both of which were markedly reduced following Yuzhu treatment (Figure 4A and B). Moreover, fasting insulin levels were significantly increased in HFD-fed mice compared with the control group, whereas Yuzhu administration markedly decreased fasting insulin levels (Figure 4C), further suggesting an improvement in IR. In addition, HFD feeding induced pronounced dyslipidemia, as evidenced by increased serum TG, T-CHO, and LDL-C levels, accompanied by decreased HDL-C levels. These alterations were significantly improved after Yuzhu administration (Figure 4D–G). Metabolic assessments further demonstrated that HFD-fed mice developed obvious glucose intolerance and IR, as reflected by increased glucose excursion and area under the curve during the GTT, as well as impaired glucose-lowering responses during the ITT (Figure 4H and I). Notably, Yuzhu treatment significantly improved both glucose tolerance and insulin sensitivity. Histopathological examination revealed marked adipocyte hypertrophy and inflammatory cell infiltration in adipose tissues of HFD-fed mice, whereas these pathological changes were substantially alleviated following Yuzhu treatment (Figure 4J). Consistently, ELISA analysis showed that the elevated levels of the pro-inflammatory cytokines IL-6 and IL-1β in adipose tissues were significantly reduced by Yuzhu administration (Figure 4K). Collectively, these findings indicate that Yuzhu effectively alleviates HFD-induced metabolic dysfunction, improves glucose and lipid homeostasis, enhances insulin sensitivity, and attenuates adipose tissue inflammation.

Figure 4 Yuzhu ameliorates HFD-induced obesity, insulin resistance, and adipose tissue inflammation in mice. (A) Changes in body weight during the experimental period. (B) Blood glucose levels. (C) Fasting insulin levels. (D–G) Serum lipid profiles, including triglyceride (TG), total cholesterol (T-CHO), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). (H) Glucose tolerance test (GTT) for evaluation of glucose tolerance. (I) Insulin tolerance test (ITT) for assessment of insulin sensitivity. (J) Representative HE staining images of adipose tissue showing adipocyte morphology and inflammatory cell infiltration. (K) ELISA analysis of the pro-inflammatory cytokines IL-6 and IL-1β in adipose tissue. Data are presented as mean ± SD. Animal experiments represent biological replicates (n = 8 mice per group). Data were analyzed using two-way ANOVA (A, G–H) and one-way ANOVA (B–F, J). * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Yuzhu Attenuates the Activation of TLR4/NLRP3 Signaling-Related Inflammatory Responses in Adipose Tissue of HFD-Fed Mice

To investigate potential mechanisms underlying the protective effects of Yuzhu, the expression of TLR4/NLRP3 signaling pathway-related molecules was evaluated in adipose tissues. RT-qPCR analysis revealed that HFD feeding markedly increased the mRNA expression levels of Tlr4, Myd88, Nlrp3, Il1b, and Tnf compared with those in the control group, indicating enhanced inflammatory signaling. Notably, Yuzhu treatment significantly reduced the expression of these genes, with the most pronounced effects observed in the high-dose group (Figure 5A). Consistent with the transcriptional results, WB analysis demonstrated that the p-p65/p65 and p-STAT3/STAT3 ratios, as well as the protein expression level of cleaved caspase-1, were significantly elevated in HFD-fed mice. Yuzhu administration markedly reduced these changes (Figure 5B). Furthermore, IHC staining showed a substantial increase in NLRP3-positive cells within adipose tissues of HFD-fed mice, which was significantly reduced following Yuzhu treatment (Figure 5C). These findings suggest that the beneficial effects of Yuzhu on adipose tissue inflammation may be associated with modulation of TLR4/NLRP3 signaling-related inflammatory responses. However, further studies are required to establish the causal involvement of this pathway.

Figure 5 Effects of Yuzhu on TLR4/NLRP3 signaling pathway-related inflammatory markers in adipose tissue of HFD-fed mice. (A) RT-qPCR analysis of Tlr4, Myd88, Nlrp3, Il1b, and Tnf mRNA expression levels in adipose tissue. (B) WB analysis of p-p65, p65, p-STAT3, STAT3, and cleaved caspase-1 protein expression. Quantitative analysis of the p-p65/p65 and p-STAT3/STAT3 ratios, as well as cleaved caspase-1 expression, was performed to evaluate inflammatory and inflammasome-related signaling pathway activation. (C) Representative IHC staining and quantitative analysis of NLRP3 expression in adipose tissue. Data are presented as mean ± SD. Animal experiments represent biological replicates (n = 8 mice per group). Data were analyzed using one-way ANOVA. **** P < 0.0001.

DG Shows Favorable Binding to TLR4 and NLRP3

Five core components were screened in this study: corchorifatty acid F, 9,10-dihydroxy-12Z-octadecenoic acid, N-trans-feruloyloctopamine, N-cis-feruloyloctopamine, and DG. Since N-trans-feruloyloctopamine and N-cis-feruloyloctopamine are listed as the same compound in the PubMed database, only N-trans-feruloyloctopamine was analyzed for molecular docking. The results showed that DG had the lowest binding energy with TLR4/NLRP3, indicating the strongest binding affinity among the candidate molecules (Table 2). Overall, all four compounds demonstrated favorable binding capacities to both TLR4 and NLRP3, suggesting that they may serve as important bioactive constituents responsible for the pharmacological effects of Yuzhu. The representative docking conformations of the compounds with TLR4 are shown in Figure 6A, whereas their docking conformations with NLRP3 are presented in Figure 6B. Hydrogen bond interactions are highlighted in the docking models. The TLR4–N-trans-feruloyloctopamine complex was excluded because no hydrogen bond interaction was observed.

Table 2 Molecular Docking Affinity Scores

Figure 6 Molecular docking analysis reveals favorable interactions between the core active components of Yuzhu and the TLR4/NLRP3 targets. (A) Representative binding conformations of the core active components with TLR4. (B) Representative binding conformations of the core active components with NLRP3.

DG Attenuates TNF-α-Induced IR and Inflammatory Responses in Adipocytes

To validate the biological activity of the core compound identified by molecular docking, the effects of DG were investigated in TNF-α-induced adipocytes. Microscopic observation and oil red O staining confirmed the successful differentiation of 3T3-L1 preadipocytes into mature adipocytes characterized by abundant intracellular lipid droplets (Figure 7A and B). Following TNF-α stimulation, glucose uptake was significantly reduced, indicating successful establishment of an IR model (Figure 7C). Based on the molecular docking results showing the strongest binding affinity of DG to TLR4 and NLRP3, DG was selected for subsequent validation. CCK-8 analysis demonstrated that DG exhibited no significant cytotoxicity at concentrations ranging from 0 to 60 μg/mL, whereas higher concentrations (80 and 100 μg/mL) reduced cell viability (Figure 7D). Therefore, concentrations of 5, 20, and 60 μg/mL were selected for further experiments. Notably, DG treatment significantly alleviated TNF-α-induced suppression of cell viability in a dose-dependent manner (Figure 7E). In addition, ELISA analysis revealed that TNF-α markedly increased the secretion of IL-6 and IL-1β, whereas DG treatment significantly reduced the levels of both cytokines (Figure 7F). These findings indicate that DG effectively protects adipocytes against TNF-α-induced injury and inflammatory responses.

Figure 7 Diosgenin glucoside (DG) attenuates TNF-α-induced insulin resistance and inflammatory responses in adipocytes. (A) Representative microscopic images showing successful differentiation of 3T3-L1 preadipocytes into mature adipocytes. (B) Oil red O staining confirming intracellular lipid accumulation and adipocyte maturation. (C) Glucose uptake assay demonstrating successful establishment of the TNF-α-induced insulin resistance model. (D) CCK-8 analysis evaluating the cytotoxicity of DG at different concentrations (0–100 μg/mL). (E) Effects of DG on cell viability in TNF-α-treated adipocytes. (F) ELISA analysis of the pro-inflammatory cytokines IL-6 and IL-1β in TNF-α-stimulated adipocytes following DG treatment. Data are presented as mean ± SD. Cell experiments were performed in three independent biological replicates, each with three technical replicates. Data were analyzed using one-way ANOVA. ns P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

DG Modulates TLR4/NLRP3 Signaling-Related Responses and Protects Adipocytes from TNF-α-Induced Injury

To further explore the potential involvement of TLR4/NLRP3 signaling in the protective effects of DG, the expression of pathway-related genes and adipocyte apoptosis were evaluated. RT-qPCR analysis demonstrated that TNF-α stimulation significantly increased the mRNA expression levels of Tlr4, Nlrp3, and Il1b compared with those in the control group, indicating enhanced inflammatory signaling. Treatment with high-dose DG (DG-H) markedly reduced the expression of these genes (Figure 8A). However, the addition of rHMGB1, a TLR4 activator, partially reversed these effects and increased the expression levels of Tlr4, Nlrp3, and Il1b, suggesting that activation of TLR4 signaling may attenuate the anti-inflammatory effects of DG. Consistent with these findings, TUNEL staining revealed a significant increase in apoptotic cells following TNF-α treatment, whereas DG-H markedly reduced adipocyte apoptosis (Figure 8B). Notably, rHMGB1 administration partially diminished the anti-apoptotic effect of DG, resulting in a higher apoptosis rate compared with that in the DG-H group. Collectively, these findings suggest that the protective effects of DG against TNF-α-induced inflammatory injury and apoptosis may be associated with modulation of TLR4/NLRP3 signaling-related responses. However, additional studies are needed to further clarify the causal role of this pathway in mediating the actions of DG.

Figure 8 Effects of diosgenin glucoside (DG) on TLR4/NLRP3 signaling-related responses and adipocyte apoptosis induced by TNF-α. (A) RT-qPCR analysis of Tlr4, Nlrp3, and Il1b mRNA expression in adipocytes following TNF-α stimulation, DG treatment, and rHMGB1 intervention. (B) Representative TUNEL staining images and quantitative analysis of apoptotic adipocytes under different treatment conditions. The addition of rHMGB1 partially reversed the inhibitory effects of DG on inflammatory gene expression and apoptosis. Data are presented as mean ± SD. Cell experiments were performed in three independent biological replicates, each with three technical replicates. Data were analyzed using one-way ANOVA. ** P < 0.01, *** P < 0.001, **** P < 0.0001.

Discussion

The global prevalence of obesity and T2DM continues to rise.39 Adipose tissue chronic low-grade inflammation is a core driver of this metabolic dysregulation, making it a major focus of research.40 Although the TLR4/NLRP3 inflammasome pathway has been recognized as a key regulator of adipose inflammation and IR,41 drugs targeting single molecules often show limited clinical translation due to the complexity of metabolic networks. Natural products, with their inherent advantage of synergistic multi-component, multi-target actions, represent a vital source for developing novel intervention strategies for metabolic diseases.42Polygonati Odorati Rhizoma (Yuzhu), a traditional Chinese medicinal herb used for nourishing yin and moistening dryness, has shown promise in improving metabolic parameters in both clinical and preliminary experimental studies.23,43 Our study integrated UHPLC-Q-TOF-MS/MS, network pharmacology, molecular docking, and functional experiments to investigate the active constituents and potential mechanisms of Yuzhu. The results identified DG as a potential bioactive constituent and suggested that modulation of the TLR4/NLRP3 signaling pathway may be involved in the beneficial effects of Yuzhu on obesity-associated IR. Our findings demonstrate that Yuzhu ameliorates adipose tissue inflammation, obesity, and IR and is associated with reduced expression of TLR4/NLRP3 pathway-related inflammatory markers. While these observations provide mechanistic insights into the potential actions of Yuzhu, further studies are required to establish the causal role of the TLR4/NLRP3 pathway. Nevertheless, the present study provides a component-target-effect framework that may facilitate future investigations into the pharmacological basis of Yuzhu and support the development of natural product-based interventions for metabolic disorders.

This study represents the first systematic identification of 127 chemical constituents in Yuzhu extract in both ion modes. Pharmacokinetic screening yielded 67 potential active components spanning multiple classes, including amino acid derivatives, phenolic acids, and steroidal saponins. This provides a more comprehensive profile than previous studies focused on single or limited compound classes.44 Topological analysis of the network pharmacology model highlighted five components with the highest degree values: corchorifatty acid F, 9,10-dihydroxy-12Z-octadecenoic acid, N-trans-feruloyloctopamine, N-cis-feruloyloctopamine, and DG. These are proposed as the core material basis for Yuzhu’s pharmacological effects. Among them, DG showed the most favorable molecular docking scores with TLR4 and NLRP3, forming stable hydrogen-bond interactions. This provides structural biology evidence for its direct potential to modulate the pathway. DG, a representative steroidal saponin in Yuzhu, is a recognized agent with anti-inflammatory and anti-diabetic properties.45 In diabetic mice, long-term treatment with DG was also shown to improve glucose tolerance and lipid profiles, reduce IL-1β, IL-6, and TNF-α levels, and exert anti-inflammatory effects against myocardial injury.46 Our findings address a previous shortcoming in Yuzhu research, which emphasized compound identification over the functional screening of core, pharmaceutically relevant active components.

Enrichment analysis demonstrated that the targets of Yuzhu’s active components were significantly associated with inflammatory pathways, particularly the NOD-like receptor and Toll-like receptor pathways. The core targets TLR4 and CCL2 were closely associated with the TLR4/NLRP3 signaling axis. In vivo experiments showed that HFD feeding significantly increased the expression of TLR4, MyD88, and NLRP3 in mouse adipose tissue, accompanied by increased p65 and STAT3 phosphorylation and elevated release of IL-1β and IL-6. Yuzhu extract intervention dose-dependently reversed these changes, reduced adipocyte hypertrophy and inflammatory infiltration, and improved glucose tolerance and insulin sensitivity. These findings align with prior reports indicating that chronic HFD induces inflammatory injury and elevates inflammatory cytokines by activating the TLR4/NLRP3 pathway, an effect exacerbated by TLR4 overexpression.47 Furthermore, it has been demonstrated that Shenxiong Yujing Granule, a formulation containing Polygonati Odorati Rhizoma, can enhance the survival rate and stimulate the differentiation of newly proliferating neurons in rat models of diabetes-associated cerebral ischemia presenting with Qi-Yin deficiency and collateral obstruction by blood stasis.48 In vitro, the core component DG reversed the TNF-α-induced inflammatory response in 3T3-L1 adipocytes. DG treatment was associated with reduced expression of TLR4/NLRP3 pathway-related molecules and decreased cellular apoptosis. Importantly, the protective effect was partially antagonized by co-treatment with rHMGB1, a TLR4 ligand. These findings suggest that modulation of TLR4/NLRP3 signaling may contribute, at least in part, to the beneficial effects of DG and Yuzhu. However, further studies are required to establish the causal involvement of this pathway.

This mechanism aligns closely with the known pathophysiology of the TLR4/NLRP3 axis.49 TLR4, an innate immune receptor, recognizes DAMPs accumulating in dysfunctional adipose tissue. Its activation promotes NLRP3 inflammasome assembly, often via nuclear factor kappa B signaling.50 NLRP3 activation then leads to caspase-1 cleavage and IL-1β maturation, which directly inhibits insulin receptor substrate phosphorylation, exacerbating IR.51 In the present study, Yuzhu extract and its core component DG were associated with reduced expression of TLR4/NLRP3 pathway-related inflammatory markers. These findings suggest that modulation of TLR4/NLRP3 signaling may contribute to the anti-inflammatory and insulin-sensitizing effects of Yuzhu. However, further studies are required to determine whether this pathway plays a causal role in mediating the observed metabolic benefits.

Our study provides preliminary experimental evidence supporting the potential application of Yuzhu in obesity-associated IR. First, as a food-medicine herb, Yuzhu has a favorable safety profile and wide availability.52 Its core component, DG, was identified as a potential bioactive constituent associated with the observed beneficial effects. Second, the TLR4/NLRP3 pathway has been recognized as an important therapeutic target in metabolic disorders.53 The present findings suggest that modulation of this pathway may contribute to the beneficial effects of Yuzhu, although further mechanistic studies are required to establish causal relationships. Finally, the integrated strategy combining MS, network pharmacology, molecular docking, and experimental validation may provide a useful framework for investigating the active constituents and potential mechanisms of other natural products.

However, this study has certain limitations that should be acknowledged. First, although 127 chemical constituents were identified in Yuzhu extract by UHPLC-Q-TOF-MS/MS, comprehensive chemical standardization, quantitative profiling of major constituents, marker compound validation, and batch-to-batch consistency assessments were not performed. Furthermore, the in vitro experiments focused solely on the single component DG, leaving the potential synergistic effects among the core constituents unexplored. Accordingly, future research should prioritize investigating combinations of core components to elucidate dose-effect relationships and synergistic regulation of the TLR4/NLRP3 pathway. Second, although fasting glucose tolerance, insulin sensitivity, fasting insulin levels, and lipid metabolism were evaluated, other metabolic parameters, including adiposity index, liver histology, and serum liver injury markers, were not assessed. Therefore, the metabolic benefits of Yuzhu could not be comprehensively characterized, and future studies should incorporate these evaluations to provide a more complete assessment of its therapeutic effects. In addition, although the present findings suggest that Yuzhu treatment is associated with suppression of the TLR4/NLRP3 signaling pathway, the current evidence remains primarily correlational and does not establish a direct causal relationship. This interpretation is supported by previous studies demonstrating that activation of the TLR4/NLRP3 signaling pathway plays a pivotal role in obesity-associated inflammation and insulin resistance.54,55 Nevertheless, further studies using pathway-specific inhibitors, gene silencing, or genetic models are warranted to provide more direct mechanistic evidence. Most importantly, clinical data are absent, leaving the pharmacokinetic profiles, bioavailability, and effective doses of the core components in clinical settings undefined. Therefore, future studies should evaluate the long-term efficacy, safety, and pharmacokinetic characteristics of Yuzhu extract and DG, as well as their effects on glucose metabolism and inflammatory markers in patients with obesity and IR, thereby facilitating clinical translation.

In conclusion, Yuzhu extract ameliorated obesity-associated metabolic dysfunction and IR in HFD-induced mice and exerted anti-inflammatory effects through multi-component and multi-target regulation. DG may represent a key bioactive constituent contributing to these protective effects. The findings suggest that modulation of the TLR4/NLRP3 signaling pathway may be involved in the beneficial actions of Yuzhu, providing experimental evidence and mechanistic insight for the further development of Yuzhu-derived interventions for metabolic disorders.

Conclusion

The present study demonstrated that Yuzhu extract improved metabolic and inflammatory abnormalities and alleviated IR in HFD-induced mice. Through integrated chemical profiling, network pharmacology, molecular docking, and experimental validation, DG was identified as a potential bioactive constituent associated with these effects. The findings suggest that regulation of the TLR4/NLRP3 signaling pathway may contribute to the beneficial actions of Yuzhu. While further mechanistic and clinical studies are required, this work provides experimental evidence supporting the potential of Yuzhu and its active constituents as candidates for the management of obesity-associated IR.

Data Sharing Statement

The data used and/or analyzed during the current study are available from the corresponding author.

Ethics Statement

All animal procedures were approved by the Institutional Animal Care and Use Committee of Hunan Evidence-based Biotechnology Co., Ltd (Ethics Number: XZ258376). All animal handling and experimental protocols adhered to the recommendations set forth in the Guidelines for the Care and Use of Laboratory Animals.

Author Contributions

Xu Deng: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing – original draft.

Yuntao Luo: Conceptualization, Supervision, Funding acquisition, Project administration, Writing – review & editing.

Hua Luo: Supervision, Validation, Writing – review & editing.

Yong Wu: Investigation, Methodology.

Xiao Chen: Data curation, Formal analysis.

Jianqiang Zeng: Software, Visualization.

Weihua Li: Validation, Resources.

Xinghui Wang: Investigation, Resources.

All authors made a significant contribution to the work reported, whet