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. 2026 Mar 4;399(8):12107–12123. doi: 10.1007/s00210-026-05111-4

Dendrobium officinale polysaccharide ameliorate hyperlipidemia through LKB1/AMPK and CD36/PGC-1α/UCP1 activation and gut microbiota modulation

Yumeng Gong 1, Hong Fang 1, Liujin Wu 2, Jiamin Zhang 1, Yihui Wen 1, Yi Rao 2,✉, Yi Zhao 1,✉
PMCID: PMC13269397  PMID: 41779140

Abstract

Dendrobium officinale polysaccharide (DOP) is recognized for its potential therapeutic effects on hyperlipidemia; however, its specific mechanisms in modulating lipid metabolism within hepatic and adipose tissues, along with its influence on the gut microbiota, have yet to be fully elucidated. Our study identified that the monosaccharide constituents of DOP comprise mannose, glucose, rhamnose, galactose, xylose, and arabinose. Network pharmacology analysis further indicated that the AMPK signaling pathway serves as a principal target of DOP. Using SD rats (8 weeks old, weighing 220–250 g), a hyperlipidemia model was established through a high-sugar, high-fat diet. Administration of DOP for 45 days resulted in significant reductions in total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), accompanied by an elevation in high-density lipoprotein cholesterol (HDL-C). Histopathological evaluation revealed an improvement in hepatic steatosis. At the molecular level within hepatic tissue, DOP treatment restored the expression of liver kinase B1 (LKB1) and the AMP-activated protein kinase (AMPK), while concurrently suppressing HMG-CoA reductase (HMGCR), sterol regulatory element-binding protein 1 (SREBF1), and phosphorylated acetyl-CoA carboxylase 1 (ACC1). In adipose tissue, DOP downregulated the cluster of differentiation 36 (CD36) expression and upregulated peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) and uncoupling protein 1 (UCP1). Gut microbiota analysis demonstrated an increased abundance of Bacteroidota, Verrucomicrobiota, and Akkermansiaceae, alongside a decrease in Firmicutes and Actinobacteriota. Correlation analyses revealed that genera were correlated with serum lipid levels. Collectively, these findings suggest that DOP mitigates hyperlipidemia through modulation of the hepatic LKB1/AMPK signaling pathway and its downstream targets HMGCR, SREBF1, and ACC1, activation of the adipose CD36/PGC-1α/UCP1 pathway, and restoration of gut microbiota homeostasis, indicating a close relationship with the gut–liver axis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00210-026-05111-4.

Keywords: Dendrobium officinale polysaccharide, Hyperlipidemia, Network pharmacology, Gut microbiota, LKB1/AMPK signaling pathway, CD36/PGC-1α/UCP1 signaling pathway

Introduction

Hyperlipidemia, a major risk factor for atherosclerosis and cardiovascular diseases, is characterized by abnormal lipid profiles, including elevated levels of total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), alongside decreased high-density lipoprotein cholesterol (HDL-C) (Wójcik-Cichy et al. 2018).

Excessive lipid accumulation in hepatic and adipose tissues is a hallmark of hyperlipidemia. Hepatic steatosis results from disrupted lipid metabolism, whereas adipose tissue dysfunction induces inflammatory responses and the release of free fatty acids, thereby establishing a deleterious feedback loop (Vial et al. 2011; Capurso and Capurso 2012). Beyond high dietary fat consumption, modern dietary patterns, including increased intake of fructose and alcohol, facilitate the translocation of bacterial components such as endotoxins into the liver via the bloodstream (Ishimoto et al. 2013; Alwahsh et al. 2025). This process ultimately contributes to hepatic inflammation, impaired insulin sensitivity, further lipid accumulation, and metabolic dysregulation.

The AMP-activated protein kinase (AMPK) signaling pathway serves as a critical metabolic regulator, influencing lipid synthesis through acetyl-CoA carboxylase (ACC) and sterol regulatory element-binding protein 1 (SREBP1), as well as promoting lipid oxidation via peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) and carnitine palmitoyltransferase 1A (CPT1A) (Yang et al. 2022; Jin et al. 2023; Yu et al. 2023). Additionally, in adipose tissue, dysregulation of the CD36/PGC-1α/UCP1 signaling pathway impairs fatty acid uptake, mitochondrial biogenesis, and thermogenesis, further contributing to systemic lipid accumulation and metabolic dysfunction (Puigserver et al. 1998; Coburn et al. 2000). Moreover, this dietary pattern induces gut microbiota dysbiosis, mediated through the gut–liver axis, aggravates lipid metabolic disorders by influencing the production of bile acids and short-chain fatty acids (SCFAs) (Wang et al. 2018; Li et al. 2020; Cao et al. 2020). Therefore, targeting metabolic signaling pathways and modulating the gut microbiota may represent significant therapeutic potential.

Dendrobium officinale, a medicinal food homologous plant officially recognized in 2023 (National Health Commission and State Administration for Market Regulation 2023), has garnered attention for its lipid-lowering properties, which are primarily attributed to its active constituent, Dendrobium officinale polysaccharides (DOP). DOP have been reported to reduce blood lipids, decrease inflammation, and influence gut bacteria (Guo et al. 2020; Li et al. 2023). However, a comprehensive understanding of its effects on lipid metabolism across key metabolic tissues, and in particular, whether DOP can simultaneously regulate lipid metabolism in both the liver and adipose tissue while also rebalancing gut microbiota, has not yet been established.

To elucidate DOP’s multitarget mechanisms against hypercholesterolemia, we employed an integrated strategy combining network pharmacology to predict core pathways, AMPK pathway verification, and 16S rRNA sequencing to assess gut microbiota, aiming to uncover the interplay between metabolic regulation and microbial remodeling.

Materials and methods

Reagents and materials

D-Anhydrous glucose (50–99-7, National Institutes for Food and Drug Control of China, 100% purity); D-mannose (3458–28-4, National Institutes for Food and Drug Control, 100% purity); galactose (GBW10064, National Institute of Metrology, China, purity 99.5%); D-xylose (6763–34-4, Chengdu Aikeda, purity 98.0%); L-( +)-arabinose (5328–37-0, Chengdu Aikeda, purity 99.0%); rhamnose (6155–35-7, McLean, purity ≥ 98%); D-galacturonic acid (91,510–62-2, Macklin, purity 97%); D( +)-fucose (3615–37-0, McLean, purity 98%); D-glucuronic acid (6556–12-3, Macklin, 98% purity); dextran standard (set) (140,877, National Institutes for Food and Drug Control, China); BCA Protein Quantitation Kit (2,309,002, Solarbio); SDS-PAGE gel electrophoresis system and reagents; ultra-sensitive ECL substrate (ATWI01081, abbkine); HMGCR antibody (sc-271595, SANTA CRUZ); LKB1 antibody (10,746–1-AP, PROTEINTECH); AMPK antibody (10,920–2-AP, PROTEINTECH); β-actin antibody (10,025,419, PROTEINTECH); P-AMPK antibody (AB133448, ABCAM); SREBF1 antibody (66,875–1-Ig, PROTEINTECH); ACC1 antibody (67,373–1-Ig, PROTEINTECH); p-ACC1 antibody (AF3421, Affinity); CD36 antibody (R381350, Zenbio); PGC-1α antibody (66,369–1-Ig; PROTEINTECH); CPT1A antibody (15,184–1-AP, PROTEINTECH); PPAR gamma antibody (340,844, Zenbio); UCP1 antibody (23,673–1-AP, PROTEINTECH); Goat Anti-Mouse IgG/HRP Conjugate (131,224, Zhongshan Golden Bridge); and Goat Anti-Rabbit IgG/HRP Conjugate (231400103, Zhongshan Golden Bridge).

Extraction of polysaccharide from Dendrobium officinale

In the reflux extraction process (Zhao et al. 2019a), 300 g of Dendrobium officinale plant material was mixed with 6 L of purified water, maintaining a 20:1 ratio of water to plant material. This mixture was allowed to sit for two hours, after which the procedure was repeated. After suction filtration, the concentrated supernatant was precipitated with anhydrous ethanol at a volume four times that of the supernatant, stirred, and then stored at 4 °C overnight. After refrigeration, the precipitate was collected by decanting the supernatant and squeezing it dry. The resulting Dendrobium officinale polysaccharide (DOP) was obtained through freeze-drying (Zhao et al. 2019b). The total polysaccharide content in Dendrobium officinale was measured to be 25.3% using the phenol-concentrated sulfuric acid method.

Determination of monosaccharide components in DOP

Then, 0.3 g of freeze-dried crude polysaccharide powder from Dendrobium officinale was accurately weighed and placed in a conical flask; then, precisely 50 mL of water was added and the mixture was heated and shaken to dissolve completely. A precise 10 mL was taken and placed in a 100-mL volumetric flask, 2 mL of the internal standard solution was accurately added, dissolved in water, and diluted to the mark, then thoroughly mixed. Glucosamine hydrochloride was utilized as the internal standard solution. Solutions of D-anhydroglucose, D-mannose, galactose, D-xylose, L-(-)-arabinose, rhamnose, D-galacturonic acid, D(-)-fucose, and D-glucuronic acid were prepared as required and combined to serve as the reference solution. The analysis was performed using a Shimadzu LC-20A liquid chromatograph. Chromatographic separation was achieved on an Agilent XDB-C18 column (4.6 × 250 mm, 5-μm particle size). The mobile phase comprised 0.1% phosphate buffer, adjusted to pH 6.70 with phosphoric acid, and acetonitrile in an 85:15 (v/v) ratio. The flow rate was set at 0.8 mL/min, with detection conducted at a wavelength of 250 nm. The column temperature was maintained at 30 °C throughout the analysis. Weigh an appropriate amount of dextran molecular weight standards with known molecular weights (set) D1, D2, D3, D4, D5, D6, D7, D8, D2000 series and dissolve them in the mobile phase to prepare a 10 mg/mL standard solution. The standard curve regression equation obtained is as follows: Y = 0.00111X3—0.0416X2 + 0.0443X + 9.625 (R2 = 0.997, R = 0.998).

Network pharmacology

A systematic review of the chemical constituents of Dendrobium officinale was conducted utilizing multiple bibliographic databases, including the China National Knowledge Infrastructure, Wanfang Database, and PubMed. Relevant literature was thoroughly analyzed and synthesized to elucidate the chemical composition of Dendrobium officinale. All identified compounds were cataloged in PubChem and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform to obtain structure-data file (SDF) structural data (Li et al. 2022); alternatively, three-dimensional molecular structures were generated using King Draw software to establish a comprehensive compound library for Dendrobium officinale. The identified constituents were imported in SDF or Mol file formats into SwissADME for ADME (absorption, distribution, metabolism, excretion) screening. A high gastrointestinal (GI) absorption score was interpreted as indicative of strong oral bioavailability. Compounds exhibiting positive results in at least two of five drug-likeness criteria (Lipinski, Ghose, Veber, Egan, and Muegge) were considered to possess favorable pharmacological properties. Bioactive targets of Dendrobium officinale were predicted via SwissTargetPrediction, facilitating the construction of an integrated effective component–target library. Concurrently, a comprehensive repository of hyperlipidemia-related targets was compiled by integrating data from three authoritative databases: GeneCards, OMIM, and DrugBank (Rappaport et al. 2017). Systematic literature analysis further identified key therapeutic targets closely linked to disease progression.

For protein–protein interaction (PPI) network analysis, common targets from the Dendrobium officinale effective component–target library and the hyperlipidemia target database were uploaded to the STRING database through Cytoscape 3.9.1 software, applying an interaction confidence threshold exceeding 0.9. The PPI network was constructed using the Network Analysis module, with threshold parameters set at twice the median values for degree, betweenness centrality, and closeness centrality (Sun et al. 2020). The network was refined by removing disconnected nodes to enhance structural coherence.

Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed using the Metascape platform, which integrates multilevel annotations and pathway resources. KEGG analysis identified biologically significant pathways associated with the target genes, while GO analysis systematically evaluated gene functions across biological processes, cellular components, and molecular functions. Intersection targets were submitted to STRING to obtain detailed protein–protein interaction information (Yuan et al. 2020). Finally, the “effective component–intersection target–pathway” network was visualized using Cytoscape (version 3.9.1).

Molecular docking between the active components of Dendrobium officinale and the core target HMGCR was conducted. The three-dimensional structure of HMGCR was retrieved from the RCSB PDB database (https://www.rcsb.org/). Water molecules and original ligands were removed from the protein structure using PyMol software. The chemical structures of the active components (in Mol2 format) were obtained from the TCMSP database. Hydrogen atoms were added, and the macromolecular structures were prepared using AutoDock software. Molecular docking calculations were then performed. The docking conformation with the lowest binding energy was selected as the most favorable interaction model, and the results were visualized using PyMol.

The predictions from network pharmacology were subsequently tested through in vivo experiments. Specifically, the central role of the AMPK pathway identified in silico was functionally validated by measuring the expression of key proteins (LKB1, AMPK, p-AMPK, HMGCR, ACC1, and SREBF1) in liver tissue and (CD36, PGC-1α, CPT1A, and UCP1) in adipose tissue via Western blot analysis in hyperlipidemic rats.

Animal model, grouping, and experimental protocol

Seventy healthy male Sprague–Dawley rats (specific pathogen-free, 8 weeks old, weighing 220–250 g) were obtained from Hunan SJA Laboratory Animal Co., Ltd. (Hunan, China; animal license SLKJD093). The rats were kept in a controlled environment (temperature of 24 ± 2 °C, humidity of 50 ± 10%) with a 12-h light/dark cycle and had unrestricted access to food and water. All procedures involving the animals adhered to the Guidelines for the Care and Use of Experimental Animals and were approved by the Experimental Animals Ethics Committee of Jiangxi University of Traditional Chinese Medicine (Approval number: TEMPOR20230083).

After a 1-week acclimatization period under standard laboratory conditions, the animals were randomly assigned to six groups (n = 11 per group initially) using a computer-generated random number table to ensure balanced initial body weight distribution. The groups were as follows: a normal control group fed a standard diet (control), and five groups fed a high-fat diet: a hyperlipidemia model group (HLP), a positive control group treated with simvastatin (SIM, 4 mg/kg/day), and three DOP-treated groups at high, medium, and low doses (H-DOP, M-DOP, L-DOP at 4, 2, and 1 g/kg/day, respectively). DOP doses were converted from human dosages based on body surface area. The standard diet contained 17% protein, 11% fat, 3% fiber, 6.5% ash, and 2.5% minerals, while the high-fat diet comprised 20.0% sucrose, 15% lard, 1.2% cholesterol, and 0.2% sodium taurocholate (Kritchevsky et al. 1982), along with suitable amounts of casein, calcium hydrogen phosphate, and ground limestone (Wuxi Fan Bo Biotechnology Co., Ltd.). The raw materials for Dendrobium officinale were procured from Huarun Jiangzhong Pharmaceutical Group Co., Ltd. (No. 20230717), and Simvastatin tablets were sourced from LeSaiXian (No. 20221201), with each tablet containing a dosage of 10 mg.

Hyperlipidemia was induced by feeding the high-fat diet for 22 days. Model success was confirmed at the end of this induction period by measuring serum total cholesterol (TC) and triglycerides (TG) in a subset of rats from the model group (n = 6), which showed significant elevations compared to the normal control group. Subsequently, drug interventions (simvastatin or DOP) were administered via oral gavage daily for a further 45-day treatment period, while the high-fat diet feeding was maintained throughout to sustain the hyperlipidemic state. The control group received ultrapure water via gavage. Body weight was recorded weekly. The investigators responsible for outcome assessments (histopathology, Western blot, and serum lipid analysis) were blinded to group allocation. Data from all animals that completed the protocol were included. For final biochemical, molecular, and histological analyses, six animals were randomly selected from each group (n = 6 per endpoint).

At the end of the treatment period and following a 24-h fast, rats were anesthetized with an intraperitoneal injection of 1.25% Avertin. Blood samples were collected via abdominal aortic puncture using sterile syringes. Let the blood sit at room temperature for 30 min to allow it to clot, then centrifuge it at 3000 rpm at 4 °C in a low-temperature (Eppendorf Centrifuge 5424 R) high-speed centrifuge for 10 min to obtain the serum. Serum aliquots were stored at − 80 °C until analysis. Liver and epididymal adipose tissue were rapidly excised, weighed, and either fixed in 4% paraformaldehyde for histology or stored at − 80 °C for molecular analysis.

Measurement of serum lipid parameters

The serum was collected and used for the measurement of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) using commercial assay kits according to the manufacturer’s instructions.

Liver tissue pathology

Liver specimens were fixed overnight in a 4% paraformaldehyde solution. Following fixation, dehydration, and paraffin embedding, liver Sects. (4-μm thick) were prepared and stained with hematoxylin and eosin (HE) for general morphology. Parallel sections were stained with Oil Red O to visualize neutral lipids.

Western blot

A lysis solution, consisting of tissue lysis buffer and an inhibitor in a 50:1 ratio, was added to 60 mg of liver or adipose tissue. The mixture was homogenized 3–5 times over 10 min. The sample was then centrifuged at 4 °C at 15,000 rpm for 10 min to obtain the supernatant. The protein concentration was measured using a BCA protein quantification kit. Proteins were separated using SDS polyacrylamide gels, with electrophoresis conducted at 70 V for 30 min, followed by an increase to 100 V until the bands were adequately separated. The separated bands were transferred at 270 mA for 2 h. The samples were then blocked with 5% skim milk at room temperature for 1 h, followed by washing with TBST. Primary antibodies (AMPK, p-AMPK, LKB1, HMGCR, ACC1, and p-ACC1; CD36, PGC-1α, CPT1A, PPAR-γ, and UCP1) were incubated overnight, and a secondary antibody (1:5000) was added and incubated for 1.5 h at room temperature before washing with TBST. An ECL reagent was used to detect antibody–antigen complexes, and grayscale values of the target and internal control bands for each protein were calculated.

16S rRNA high-throughput sequencing

The gut microbiota profile was characterized through 16S rRNA high-throughput sequencing, and α-diversity indices (Shannon, Simpson, Ace, Chao, and Sobs) and β-diversity were used to assess microbial richness and evenness. Taxonomic profiling further detailed bacterial communities within cecal samples. Spearman correlation analysis quantified associations between serum biochemical parameters and cecal microbiota composition.

Statistical analysis

All data were processed using GraphPad 8.0 and IBM SPSS Statistics 26.0, presented as mean ± standard deviation (SD). Intergroup comparisons across six cohorts were analyzed using one-way ANOVA followed by Tukey’s post hoc test for multiple comparisons, with statistical significance set at P < 0.05.

Results

Composition and analysis of polysaccharides from Dendrobium officinale

As shown in Fig. 1, the monosaccharide components found in the crude polysaccharides of Dendrobium officinale are mannose, glucose, rhamnose, galactose, xylose, and arabinose, with contents of 52.8%, 15.4%, 0.44%, 0.58%, 0.19%, and 0.47%, respectively. The measured weight-average molecular weight (Mw) of Dendrobium officinale polysaccharides is 728.354 kDa, and the number-average molecular weight (Mn) is 190.246 kDa, based on a dextran standard curve.

Fig. 1.

Fig. 1

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a Monosaccharide derivatives were prepared by 1-phenyl-3-methyl-5-pyrazolone (PMP) precolumn derivatization for HPLC analysis (1-mannose, 2-glucosamine hydrochloride (internal standard), 3-rhamnose, 4-glucuronic acid, 5-galacturonic acid, 6-glucose, 7-galactose, 8-xylitol, 9-arabinose, and 10-fucose). (b) Graph magnification

Network pharmacology

A total of 89 chemical constituents were preliminarily identified from Dendrobium officinale. After ADME screening and the exclusion of nontarget compounds, 54 bioactive components were retained, encompassing alkaloids, terpenes, polysaccharides, flavonoids, phenols, and bibenzyls. Using the SwissTargetPrediction and BATMAN-TCM databases, 553 unique target proteins of these active compounds were identified. Meanwhile, 517 hyperlipidemia-related targets were obtained from the GeneCards, OMIM, and DrugBank databases. The intersection of the two datasets yielded 81 potential targets, as shown in Fig. 2a.

Fig. 2.

Fig. 2

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a The Venn diagram of Dendrobium officinale and hyperlipidemia. (b) Proteins ranked high according to their degree values. (c) GO enrichment analysis. (d-e) KEGG pathway analysis. (f) Active ingredient-intersection target–pathway network

To identify key proteins in the PPI network constructed from overlapping gene targets, the top-ranking core nodes were screened, including ALB, TNF, PPARG, PPARA, ESR1, EGFR, HMGCR, MMP9, CYP3A4, SCARB1, ACE, TGFB1, TLR4, SERPINE1, HSP90AA1, NOS3, PPARD, ERBB2, NR1F4, and FABP1 (Fig. 2b). Notably, many of these hub proteins play critical roles in lipid biosynthesis, fatty acid oxidation, and inflammatory regulation. PPARs, FXR, HMGCR, and SCARB1 are central regulators of lipid synthesis, oxidation, storage, and transport, driving core metabolic dysregulation. Meanwhile, excess lipids activate signaling pathways mediated by TNF and TLR4, which trigger chronic inflammation and disrupt normal insulin and lipid metabolic signaling. These findings imply that Dendrobium officinale may exert its hypolipidemic activity by simultaneously modulating lipid metabolic processes and inflammation-associated pathways.

Functional enrichment analysis using GO and KEGG databases further supported these observations. The intersecting gene targets were predominantly enriched in metabolism-related pathways and signaling cascades, among which the AMPK signaling pathway ranked among the most significant (Figs. 2d–2f). AMPK functions as a central energy sensor, coordinating fatty acid oxidation and lipid synthesis according to cellular energy demands. The prominence of this pathway in enrichment results suggests that AMPK may represent a principal molecular target through which Dendrobium officinale regulates lipid homeostasis. Taken together, these results indicate that the lipid-lowering action of Dendrobium officinale is likely mediated by modulation of an AMPK-centered metabolic network that integrates lipid metabolism, oxidative stress, and inflammatory signaling.

Molecular docking was performed between the monosaccharide components of Dendrobium officinale polysaccharides and the core target HMGCR, and the results are shown in Fig. 3. The binding energies for mannose, glucose, rhamnose, galactose, xylose, and arabinose were − 6.3, − 6.2, − 5.8, − 5.9, − 5.5, and − 6.2 kcal/mol, respectively. With all binding energies below − 5 kcal/mol, the results indicate strong binding, high biological affinity, and stable molecular conformations between the core components and the target protein.

Fig. 3.

Fig. 3

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Visualization of DOP monosaccharide components docking with key target molecules. (a) Mannose acts on HMGCR. (b) Glucose acts on HMGCR. (c) Rhamnose acts on HMGCR. (d) Galactose acts on HMGCR. (e) Xylose acts on HMGCR. (f) Arabinose acts on HMGCR

These in silico predictions pointed to the AMPK signaling pathway as a central target of DOP. To functionally validate this prediction, we next examined the expression of key proteins in this pathway in liver and adipose tissues of experimental rats.

Body weight, serum lipid levels, and histopathological changes in the liver

As demonstrated in Fig. 4a, the HLP group exhibited a significantly higher average body weight compared to the control group, with notable increases relative to both the DOP and SIM treatment groups. The hypolipidemic effects of DOP were further examined through serum lipid measurements, including TC, TG, LDL-C, and HDL-C levels (Fig. 4b). Compared to the control group, rats in the HLP group displayed significantly elevated levels of TC, TG, and LDL-C (P < 0.01), along with a moderate decrease in HDL-C levels. Administration of M-DOP, similar to the reference drug, significantly reduced atherogenic lipids TC, TG, and LDL-C (P < 0.05), while increasing HDL-C levels. Notably, the high- and low-dose groups did not show further differences in TC and LDL-C reduction, although all DOP-treated groups exhibited a significant decline in TG (P < 0.01). These findings indicate that DOP effectively improves serum lipid profiles, supporting its potential utility in preventing metabolic lipid disorders.

Fig. 4.

Fig. 4

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The changes in body weight and serum lipid levels and pathological analysis of the liver. (a) The changes in body weight during the experiment. (b) The TC, TG, HDL-C, and LDL-C levels in serum (n = 6). (c) HE staining. (d) Oil Red O staining. Values are shown as the mean ± SD. Compared with the control group: *P < 0.05, **P < 0.01. Compared with the HLP group: #P < 0.05, ##P < 0.01 by one-way ANOVA followed by Tukey’s post hoc tests

Histological assessment of hepatic tissue using HE staining (Fig. 4c) revealed marked steatosis in the HLP group, with lipid droplet accumulation, cellular swelling, and vacuolar degeneration. Following DOP administration, these pathological changes were notably alleviated, as evidenced by reduced hepatocellular vacuolation and fewer lipid inclusions. To visualize hepatic triglyceride deposition, Oil Red O staining was performed (Fig. 4d). Strong red staining in the HLP group indicated excessive TG accumulation, whereas DOP-treated livers showed a clear reduction in lipid droplets. These observations corroborate the biochemical data, suggesting that DOP mitigates hepatic lipid accumulation and ameliorates the metabolic disturbances associated with diet-induced hyperlipidemia.

Comparison of routine blood test indicators among groups

Table 1 presents the changes in peripheral blood routine parameters among different groups of rats. Compared with the control group, the high-fat diet group (HLP) showed a decreasing trend in white blood cell (WBC) and lymphocyte (Lymph) counts, while neutrophil (Gran) levels were significantly elevated (P < 0.05), indicating that high-fat feeding induced a marked inflammatory response in the organism. Meanwhile, the red blood cell (RBC) count and hemoglobin (Hb) concentration in the HLP group were significantly reduced (P < 0.05), and platelet (PLT) levels were markedly increased (P < 0.05), suggesting the occurrence of erythropoietic suppression and a hypercoagulable state in the model rats. After DOP intervention, these hematological abnormalities were improved to varying degrees. In the SIM group, WBC, Lymph, and RBC levels were increased, while Gran and PLT were decreased compared with the HLP group (P < 0.05), indicating its anti-inflammatory and hematoregulatory effects. The H-DOP, M-DOP, and L-DOP groups exhibited similar trends to the SIM group, with more pronounced improvements. These findings suggest that DOP intervention effectively alleviates inflammation and hematopoietic dysfunction in rats with hyperlipidemia.

Table 1.

Comparison of routine blood test indicators among groups(n = 6, x̅ ± s)

Control HLP SIM H-DOP M-DOP L-DOP
WBC (× 109/L) 2.07 ± 0.96 1.73 ± 0.85 2.5 ± 0.47 2.72 ± 1.27 2.67 ± 0.92 3.13 ± 0.57 ##
Lymph (× 109/L) 1.17 ± 0.43 1.05 ± 0.49 1.35 ± 0.41 1.57 ± 0.56 1.43 ± 0.5 1.97 ± 0.44 ##
Gran (× 109/L) 0.4 ± 0.09 1.38 ± 0.33 ** 1 ± 0.29 1.2 ± 0.75 1.18 ± 0.31 1.2 ± 0.3
RBC (× 1012/L) 8.5 ± 0.86 6.98 ± 2.1 * 9.06 ± 0.44 ## 8.44 ± 1.59 # 9.5 ± 0.42 ## 8.74 ± 0.79#
Hb (g/L) 146.83 ± 11.81 113 ± 38.47 ** 141 ± 15.59 # 139 ± 21.69 # 153.17 ± 5.04 ## 143 ± 11.8 #
PLT (× 109/L) 1159.67 ± 110.6 1429.33 ± 143.05 * 1218.83 ± 229.49 1312.67 ± 339.78 1193 ± 102.86 1322.33 ± 240.53
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Compared with the control group: *P < 0.05, **P < 0.01. Compared with the HLP group: #P < 0.05, ##P < 0.01 by one-way ANOVA followed by Tukey’s post hoc tests

Western blot analysis

As illustrated in Fig. 5, the HLP group demonstrated reduced protein levels of LKB1 and AMPK (P < 0.05) compared to the control group, alongside increased expression of HMGCR (P < 0.01). After DOP administration, hepatic expression of LKB1, AMPK, and phosphorylated AMPK increased significantly (P < 0.05), with p-AMPK levels being 2–3 times higher than those in the HLP group, accompanied by a reduction in HMGCR (P < 0.01), SREBF1, and p-ACC1 expressions. These results indicate that DOP activates the LKB1/AMPK signaling cascade and suppresses cholesterol biosynthesis, thereby improving hepatic lipid metabolism. In adipose tissue, the HLP group showed pronounced upregulation of CD36 (P < 0.01) and downregulation of PGC-1α, CPT1A, PPAR-γ, and UCP1 (P < 0.01) compared with normal controls. Treatment with DOP reversed these alterations, reducing CD36 expression (P < 0.05) while elevating PGC-1α, CPT1A, PPAR-γ, and UCP1 (P < 0.05). These regulatory changes suggest that DOP promotes fatty acid oxidation and mitochondrial activity while limiting lipid uptake, which collectively contributes to reduced lipid accumulation under hyperlipidemic conditions.

Fig. 5.

Fig. 5

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The effects of DOP on the LKB1/AMPK signaling pathway in liver tissue and the CD36/PGC-1α/UCP1 signaling pathway in adipose tissue. (a) Immunoblot analysis of individual protein expression. (b) expression of LKB1, p-AMPK/AMPK ratio, expression of HMGCR, p-ACC1/ACC1 ratio, expression of SREBF1 (n = 6). (c) Immunoblot analysis of individual protein expression. (d) Expression of CD36, PGC-1α, CPT1A, PPAR-γ, and UCP1 (n = 6). Values are expressed as mean ± SD. A value of P < 0.05 was considered statistically significant. Compared with the control group: *P < 0.05, **P < 0.01. Compared with the HLP group: #P < 0.05, ##P < 0.01 by one-way ANOVA followed by Tukey’s post hoc tests

High-throughput sequencing of 16S rRNA

As shown in Fig. 6a. metrics of species richness, including the Ace, Chao, and Sobs indices, indicated a significantly lower taxonomic richness in the HLP group compared to the controls (P < 0.01), while the DOP group did not exhibit statistically distinct species numbers in relation to the HLP group. As illustrated in Fig. 6, the Shannon and Simpson indices demonstrated significant heterogeneity in alpha diversity between the HLP and control groups (Fig. 6b). However, comparisons of the Simpson index between the HLP and L-DOP groups revealed only marginal significance (P < 0.05).

Fig. 6.

Fig. 6

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Gut microbiota diversity. (a) Ace index, Chao index, and Sobs index (n = 6). (b) Shannon Index, Simpson Index (n = 6). (c) Beta diversity. (d) PCoA analysis. (e) NMDS analysis. A value of P < 0.05 was considered statistically significant. Compared with the control group: **P < 0.01. Compared with the HLP group: #P < 0.05 by one-way ANOVA followed by Tukey’s post hoc tests

As depicted in Fig. 6d, PCoA analysis revealed a significant separation of samples from the control group from those of the other groups, with some overlap observed between the HLP and medicated groups, suggesting similarities in their microbiomes. Nonmetric multidimensional scaling (NMDS) analysis indicated that the HLP group clustered closely together, while the medication group was surrounded by the HLP group (Fig. 6e). Collectively, these findings suggest that DOP influences the architecture of gut microbiota.

Analysis of species composition

Taxonomic composition analysis, a fundamental aspect of biodiversity assessment, systematically evaluates the dynamics of species richness and evenness (Maestre et al. 2012). Our analytical framework encompassed resolution at the Phylum and Family levels.

At the Phylum level, the predominant taxa included Firmicutes, Verrucomicrobiota, Actinobacteriota, Proteobacteria, Desulfobacterota, Patescibacteria, Bacteroidota, Campilobacterota, unclassified_k__norank_d__Bacteria, and Cyanobacteria (Fig. 7a). Compared with the control group, the HLP rats showed a clear rise in Firmicutes abundance from 91.30% to 94.02%, along with a notable increase in Actinobacteriota. In contrast, the relative proportions of Desulfobacterota and Bacteroidota declined significantly (P < 0.01). After DOP treatment, the imbalance in the Firmicutes/Bacteroidota ratio was largely corrected, accompanied by an enrichment of Verrucomicrobiota and Bacteroidota populations (Fig. 7c).

Fig. 7.

Fig. 7

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Gut microbiota. (a) The TOP10 species in Phylum level. (b) The TOP10 species in Family level. (c) The abundance of Firmicutes, Verrucomicrobiota, Actinobacteriota, Desulfobacterota, and Bacteroidota. (d) The abundance of Lachnospiraceae, Erysipelotrichaceae, Peptostreptococcaceae, and Akkermansiaceae. (e) correlation analysis heat map. (f) LDA score histogram (LDA > 4). A value of P < 0.05 was considered statistically significant. Compared with the control group: *P < 0.05, **P < 0.01. Compared with the HLP group: ##P < 0.01 by one-way ANOVA followed by Tukey’s post hoc tests

At the Family level, the dominant groups were Lachnospiraceae, Erysipelotrichaceae, Peptostreptococcaceae, Lactobacillaceae, Akkermansiaceae, Oscillospiraceae, Enterobacteriaceae, norank_o_Clostridia_UCG-014, Eubacterium_coprostanoligenes_group, and Eggerthellaceae (Fig. 7b). The HLP group displayed a significant expansion of Lachnospiraceae (P < 0.01) and Erysipelotrichaceae (P < 0.05), whereas Peptostreptococcaceae abundance decreased. Notably, Akkermansiaceae were enriched in the DOP-treated rats compared with the HLP group. Overall, DOP enhanced the growth of beneficial taxa such as Lachnospiraceae and Akkermansiaceae, while suppressing potentially harmful Erysipelotrichaceae, showing a more balanced modulatory effect than simvastatin (Fig. 7d).

Spearman correlation analysis (|r|> 0.2, P < 0.05) revealed strong positive associations between Firmicutes, Actinobacteria, and Erysipelotrichaceae with serum lipid levels (Fig. 7e). Conversely, Verrucomicrobiota, Bacteroidota, Peptostreptococcaceae, and Akkermansiaceae were negatively correlated with serum lipids, suggesting that improvements in lipid metabolism may be linked to the restoration of microbial composition.

Linear discriminant analysis (LDA) identified bacterial taxa that contributed most to group differences (Mörkl et al. 2022). Ten dominant bacterial groups were enriched in the control rats, two in the HLP group, three in the simvastatin group, five in the high-dose DOP group, six in the medium-dose DOP group, and one in the low-dose DOP group (Fig. 7f).

Discussion

Guided by network pharmacology predictions that highlighted the AMPK pathway as a central target, this study proposes an interacting mechanistic model to explain the action of DOP. We hypothesize that DOP intervention may activate the AMPK signaling pathway in both the liver and adipose tissue, directly influencing local lipid metabolism: inhibiting cholesterol and fatty acid synthesis in the liver, while promoting fatty acid oxidation and thermogenesis in adipose tissue. Simultaneously, AMPK pathway activation and its associated metabolic changes may further modulate gut microbiota composition through systemic or local signals, such as alterations in bile acid metabolism and inflammatory status, thereby promoting the enrichment of beneficial taxa. The improved gut microenvironment, in turn, may reinforce hepatic and systemic lipid metabolism regulation by enhancing barrier function. Thus, the AMPK pathway and gut microbiota are not independent but form an interconnected regulatory network that jointly maintains gut–liver metabolic homeostasis and contributes to the hypolipidemic effect. This study aims to validate key aspects of this integrated mechanistic hypothesis.

A long-term high-fat diet disrupts hepatic and intestinal lipid metabolism, promoting lipid accumulation and hepatic steatosis (Khoury et al. 2020; Maulana and Ridwan 2021). Lipids are transported in the bloodstream bound to lipoproteins: HDL mediates reverse cholesterol transport (Jing et al. 2022), while LDL delivers cholesterol to peripheral organs, where excess deposition contributes to plaque formation and vascular injury (Stone et al. 2014). In our high-sugar, high-fat diet model, sera TC, TG, and LDL-C were significantly elevated and HDL-C reduced, consistent with diet-induced dyslipidemia. DOP treatment lowered TC, TG, and LDL-C and raised HDL-C, suggesting it helps restore normal lipid transport balance, possibly by enhancing receptor-dependent LDL uptake and HDL-mediated cholesterol efflux, thereby promoting reverse cholesterol transport and reducing tissue lipid deposition.

Furthermore, DOP may improve hyperlipidemia-related hematological abnormalities, such as altered lymphocyte, red blood cell, and platelet counts, which are associated with immune dysregulation, inflammatory anemia, and a prothrombotic state (McLaughlin et al. 2014; Dworzański et al. 2021; Purdy and Shatzel 2021). The underlying mechanisms warrant further investigation.

To investigate the mechanism by which DOP reduces hyperlipidemia, we first conducted a network pharmacology analysis of Dendrobium officinale and found that AMPK is the core pathway of its mechanism. There are three downstream pathways of AMPK that have been implicated in the pathogenesis of hyperlipidemia. AMP-activated protein kinase (AMPK) mitigates hyperlipidemia through three principal downstream pathways. First, the AMPK–HMGCR axis inhibits cholesterol and triglyceride synthesis by phosphorylating HMG-CoA reductase (HMGCR), the rate-limiting enzyme in cholesterol biosynthesis, thereby decreasing endogenous cholesterol production (Pokhrel et al. 2021). Second, the AMPK–ACC pathway modulates fatty acid metabolism; AMPK phosphorylates and inhibits acetyl-CoA carboxylase (ACC), particularly the ACC1 isoform responsible for de novo fatty acid synthesis. This inhibition not only reduces fatty acid synthesis but also promotes mitochondrial fatty acid oxidation (Kim et al. 2012; Lally et al. 2019). Third, the AMPK–SREBP pathway regulates lipid synthesis at the transcriptional level. AMPK suppresses sterol regulatory element-binding protein 1a (SREBP-1a), which is implicated in cholesterol biosynthesis, and SREBP-1c, which facilitates triglyceride and phospholipid synthesis (Wang et al. 2020). Consistent with these potential mechanisms, our experimental results showed that DOP treatment was associated with increased hepatic expression of LKB1 and a significantly elevated phosphorylated AMPK (Thr172)/AMPK ratio. Concurrently, a reduction in the expression of HMGRR, SREBF1, and p-ACC1 was observed. These findings together suggest that DOP may activate the LKB1/AMPK signaling cascade and contribute to the suppression of cholesterol and fatty acid synthesis pathways, which could in part explain its beneficial effect on hepatic lipid metabolism.

In our model, a high-fat diet led to a significant increase in CD36 expression, while levels of PGC-1α, CPT1A, PPAR-γ, and UCP1 decreased. This observation suggests that excessive fatty acid uptake mediated by CD36 may inhibit PGC-1α signaling, impair mitochondrial function, and reduce β-oxidation, thereby promoting lipid accumulation (Zhang et al. 2014). The diminished expression of CPT1A restricts the transport of fatty acids into mitochondria, while the downregulation of PPAR-γ and UCP1 indicates a reduced adipogenic and thermogenic capacity, contributing to metabolic dysregulation (Schlaepfer and Joshi 2020; Kang et al. 2020). Treatment with DOP resulted in decreased CD36 expression alongside increased levels of PGC-1α, CPT1A, PPAR-γ, and UCP1. These regulatory modifications imply that DOP enhances fatty acid oxidation and mitochondrial function while limiting lipid uptake, collectively facilitating the reduction of lipid accumulation under hyperlipidemic conditions.

Available evidence suggests that high-fat diet-induced gut microbiota dysbiosis can induce “leaky gut,” allowing toxic microbial metabolites to enter the liver via portal circulation, triggering liver inflammation and lipid metabolism disturbances, ultimately leading to hyperlipidemia (Cani et al. 2007; Tilg and Moschen 2014). Numerous comprehensive studies have elucidated the roles of probiotics and traditional Chinese medicine (TCM). For instance, a specific variety of Houshan Dendrobium has been shown to ameliorate lipid metabolism disorders by restoring gut microbiota and metabolites in mice subjected to a high-fat diet (Ma et al. 2024). Additionally, berberine has been demonstrated to mitigate fatty liver hemorrhagic syndrome induced by a high-energy, low-protein diet in laying hens, with insights derived from microbiome and metabolomic analyses (Cheng et al. 2024).

In the present study, the hyperlipidemic model showed a clear imbalance in gut composition, with an elevated Firmicutes/Bacteroidota ratio. DOP treatment reversed this trend by enriching Bacteroidota and limiting Firmicutes proliferation. Beyond the phylum level, DOP significantly enriched beneficial taxa such as Verrucomicrobiota, Lachnospiraceae, and Peptostreptococcaceae, which are known contributors to SCFA biosynthesis and glycolipid regulation (Tong et al. 2018; Carneiro Dos Santos et al. 2024). The enrichment of Akkermansiaceae following DOP intervention is noteworthy: this family can degrade mucin, suppress inflammation, and reinforce gut barrier function (Duysburgh et al. 2024). The restoration of microbial balance coincided with increased SCFA levels, suggesting that DOP modifies the gut environment toward a lipid-lowering metabolic profile (Lu et al. 2022). Firmicutes are typically associated with butyrate production and lipid storage, whereas Bacteroidetes generate propionate that enters the liver to inhibit lipid and cholesterol synthesis (Tsai et al. 2021; Sun et al. 2023).

Statistical correlation analysis showed that these taxa were negatively related to serum TC, TG, LDL-C, and HDL-C ratios, supporting their lipid-regulating role. Conversely, Actinobacteriota and Erysipelotrichaceae are often linked to lipid accumulation and impaired fatty acid oxidation (Kaakoush 2015; Zheng et al. 2021), and their expansion showed positive correlations with serum lipid parameters (Zheng et al. 2021; Wang et al. 2022; Zhao et al. 2023). Lower Akkermansiaceae levels have been reported in obesity and metabolic disorders (Karlsson et al. 2012; Everard et al. 2013), whereas its restoration has been shown to mitigate diet-induced adiposity in animals (Zhao et al. 2017). Notably, our correlation analysis identified Erysipelotrichaceae, Peptostreptococcaceae, and Bacteroidota as key taxa strongly associated with serum lipid parameters. The positive correlation between Erysipelotrichaceae and atherogenic lipids (TC, TG, and LDL-C) underscores its potential role as a detrimental taxon in lipid dysregulation. Conversely, the negative associations of Peptostreptococcaceae and Bacteroidota with these lipid markers highlight their likely beneficial functions. DOP intervention appears to orchestrate a microbiota shift conducive to improved systemic lipid homeostasis.

In the current study, the high-fat diet induced characteristic pathological changes including hepatic steatosis, gut barrier damage, and microbial imbalance. These alterations are common features not only of high-fat feeding but also of metabolic disturbances caused by other dietary components. The observed efficacy of DOP in alleviating these high-fat diet-induced disturbances suggests its potential relevance for addressing metabolic disorders associated with broader dietary patterns. Future studies using models that combine multiple dietary stressors could further elucidate its protective scope. Among the three doses of DOP administered, the medium dose (M-DOP, 2 g/kg/day) demonstrated the most comprehensive therapeutic efficacy in mitigating high-fat diet-induced hyperlipidemia.

Limitation

This study acknowledges several limitations. The findings concerning AMPK activation and the modulation of gut microbiota are predominantly based on correlative data, underscoring the necessity for further comprehensive investigation. Additionally, the concentrations of key short-chain fatty acids (SCFAs) were not directly quantified but were inferred through microbial taxonomic profiling, which limits the experimental validation of the proposed mechanisms. To elucidate the mechanistic role of AMPK signaling in mediating the lipid-lowering effects of DOP and to identify its precise molecular targets, future research should incorporate in vitro hepatocyte models combined with genetic silencing techniques. Such investigations are planned to be undertaken in subsequent studies.

Conclusion

Taken together, these results indicate that DOP alleviates hyperlipidemia by regulating the hepatic LKB1/AMPK signaling pathway and its downstream effectors HMGCR, SREBF1, and ACC1, activating the adipose CD36/PGC-1α/UCP1 pathway, and restoring gut microbiota balance, thereby highlighting a significant association with the gut–liver axis. This study provides a preclinical rationale for exploring DOP as a potential dietary supplement or adjunctive agent targeting the gut–liver axis in hyperlipidemia. Future studies are necessary to optimize its dosage, assess long-term safety, and confirm efficacy in more complex models or human populations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (ZIP 18.7 MB) (18.8MB, zip)

Acknowledgements

We thank every colleague who contributed to this experiment.

Author contribution

All authors contributed to the study conception and design. **Yumeng Gong:** Writing – original draft, Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Validation. **Hong Fang:** Conceptualization, Data curation, Methodology, Formal analysis, Validation, Visualization. **Liujin Wu:** Methodology, Formal analysis, Validation, Visualization. **Jiamin Zhang, Yihui Wen:** Software, Validation, Visualization. **Yi Rao:** Funding acquisition, Resources. **Yi Zhao:** Writing – review & editing, Conceptualization, Supervision, Funding acquisition. The authors declare that all data were generated in-house and that no paper mill was used.

Funding

This work was supported by the National Natural Science Foundation of China (82560860), Naturel Science Foundation of Jiangxi Province (20242BAB25562), and Jiangxi Provincial Department of Science and Technology (20203ABC28W019).

Data availability

All source data for this work (or generated in this study) are available upon reasonable request.

Declarations

Ethics statements

All of the experiments were performed under the approved guidelines of the animal ethics committee of Jiangxi University of Chinese Medicine (JZLLSC20230743).

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yi Rao, Email: raoyi99@126.com.

Yi Zhao, Email: zhysyz2008@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (ZIP 18.7 MB) (18.8MB, zip)

Data Availability Statement

All source data for this work (or generated in this study) are available upon reasonable request.

Articles from Naunyn-Schmiedeberg's Archives of Pharmacology are provided here courtesy of Springer

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