Introduction
Hyperlipidemia, a prevalent metabolic disorder characterized by elevated serum lipids including total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C), alongside reduced high-density lipoprotein cholesterol (HDL-C), is a primary driver of atherosclerosis and cardiovascular diseases [1]. Current first-line pharmacotherapies—such as statins and fibrates—effectively lower lipid levels but are associated with side effects including myopathy, hepatic toxicity, and gastrointestinal discomfort [2]. Prolonged use also poses an increasing risk of heart disease.
Importantly, these medications only manage blood lipid levels without curing hyperlipidemia [3]. As a result, patients need continuous medication to maintain normal lipid levels, which may lead to drug tolerance and reduced efficacy over time. Poor patient adherence further exacerbates treatment outcomes.
This clinical landscape underscores the urgent need for safer, multi-targeted therapeutic strategies suitable for chronic management.
Korean Red Ginseng, the dried roots and rhizomes of the steamed cultivar of Panax ginseng Meyer., a plant of the family Araliaceae, has emerged as a promising candidate due to its broad pharmacological activities. Clinical meta-analyses confirm that Korean Red Ginseng consumption significantly reduces TC, TG, and LDL-C levels in patients with metabolic syndromes, along with improvements in markers related to blood glucose and blood pressure, in comparison to the placebo group [4,5]. Mechanistically, ginsenosides and other bioactive components are known to regulate hepatic lipid metabolism by activating key signaling pathways such as AMP-activated protein kinase (AMPK) and peroxisome proliferator-activated receptor α (PPARα), thereby improving hepatic lipid utilization and mitigating oxidative injury [4].
However, existing evidence remains fragmented. A comprehensive, systems-level understanding of how RGE's multi-targeted effects—spanning both direct hepatic actions and gut microbial modulation—work together to produce therapeutic outcomes is still lacking. Specifically, the interaction between its influence on the gut ecosystem and the resulting metabolic regulation in the liver represents a critical knowledge gap.
The gut-liver axis represents the bidirectional relationship between the gut, its microbiota, and the liver, with communication occurring via the bile ducts from the liver to the gut and the portal vein from the gut to the liver [6]. The disruption of the gut-liver axis serves as a critical link between high-fat diet (HFD)-induced dysbiosis and the development of systemic hyperlipidemia alongside hepatic steatosis. This relationship is mediated through several interconnected pathways.
Primarily, HFD-altered gut microbiota composition, such as an increased Firmicutes/Bacteroidetes ratio, is directly associated with elevated host serum cholesterol, demonstrating a profound microbial influence on systemic lipid metabolism [7]. Concurrently, intestinal dysbiosis can impair the gut barrier, increasing intestinal permeability and resulting in endotoxemia and inflammation, which are key drivers of liver injury and metabolic dysfunction [8]. Furthermore, the gut microbiota extensively modulates host physiology by regulating bile acid metabolism via enzymes like bile salt hydrolase (BSH), thereby shaping signaling through hepatic receptors such as FXR and TGR5, which are pivotal for maintaining nutrient metabolism and insulin sensitivity.
In a reciprocal manner, the composition of the gut microbiota is, in turn, affected by host bile acid levels [9]. Importantly, the administration of some specific beneficial bacteria have been shown to concurrently ameliorate HFD-induced hyperlipidemia and reduce hepatic steatosis, by modulating gut microbial ecology and enhancing beneficial metabolic pathways, including gut-liver folate and polyunsaturated fatty acid metabolism [10]. These findings collectively underscore that the gut-liver axis functions as an integrated system, whose dysregulation centrally contributes to the co-emergence of hyperlipidemia and hepatic steatosis.
Therefore, bridging the gap between the clinical need for sustainable hyperlipidemia treatments and the fragmented understanding of Korean Red Ginseng's multi-targeted effects, this study was designed to investigate the therapeutic effects of Korean Red Ginseng extract (RGE) through the lens of the gut-liver axis. We hypothesize that RGE ameliorates HFD-induced hyperlipidemia and hepatic injury by orchestrating a coordinated sequence from gut microbiota restructuring to systemic metabolic regulation. By employing an integrated approach that combines 16S rRNA sequencing, metabolomics, and molecular analysis, we aimed to delineate the relationship between RGE, the gut microbiota, and liver metabolism, thereby establishing a scientific foundation for its clinical application as a multi-targeted therapy.
Materials and methods
Animals
Forty-eight male Wistar rats (SPF level, 180–220 g) were purchased from Shanghai University of Traditional Chinese Medicine (SHUTCM) Laboratory Animal Centre, where they were housed according to Institutional Guidelines for Animal Care Compliance. The animals were kept in a controlled environment with a temperature of 22 ± 1 °C, 55 ± 5 % humidity, and a 12:12 h light-dark cycle. They had unrestricted access to a standard rodent diet and drinking water.
The study was conducted in adhere to ethical guidelines for animal welfare and was approved by the Ethics Committee for Animal Experiments of SHUTCM (No. PZSHUTCM2304170005).
Materials
Korean Red Ginseng extract (RGE) used in the study was supplied by Korea Ginseng Corporation. RGE was prepared through aqueous extraction and concentration, followed by filtration. It was then spray-dried and sieved, resulting in a yield of 50 %.
The specific components have been examined in previous studies by UPLC-QTOF-MS [11]. The test ktis for TC, TG, alanine aminotransferase (ALT), and glutamine aminotransferase (AST) were obtained from Nanjing Jiancheng Bioengineering Research Institute. The malondialdehyde (MDA) kits were sourced from Sangong Bioengineering (Shanghai) Co., Ltd.
Experimental design
After one week of adaptation, rats were randomly divided into six groups: normal control (N), HFD-induced model (M), and RGE treatment groups at doses of 125 mg/kg, 250 mg/kg, 500 mg/kg, and 1000 mg/kg, with 8 rats per group. The RGE doses were determined based on the recommended human dose (3–9 g/day), scaled to a required 5-fold equivalent per health food assessment guidelines, and adjusted for the 50 % extract yield to establish a range (125–1000 mg/kg) for dose-response assessment. The N group received normal chow and water, while the M and RGE groups were fed HFD to establish a hyperlipidemic model.
After three weeks, blood samples were collected via the orbital venous plexus to assess serum TC, TG, LDL, and HDL levels to confirm successful modeling. If the model was successfully established, the RGE treatment began. RGE was dissolved in the double distilled water and administered daily by gavage at doses of 125 mg/kg, 250 mg/kg, 500 mg/kg, and 1000 mg/kg.
The N and M groups received an equivalent volume of double-distilled water. Body weight and food intake were recorded every three days.
After 60 days of treatment, the rats were fasted for 12 h, then anesthetized and weighed. Blood samples were drawn from the abdominal aorta for serum analysis. The liver was dissected, weighed, and a portion was fixed in 4 % paraformaldehyde for histopathological examination, while the remaining liver tissue was stored at –80 °C.
Cecal feces were collected in sterile tubes, frozen in liquid nitrogen, and stored at –80 °C for subsequent analysis.
Measurement of organ index
The mass of the rat's liver was weighed at the end of the experiment, and the organ index was determined using the following formula [12]: organ index (%) = mass of organ (g)/body weight of rat (g) × 100 %.
Biochemical parameters detection in serum and liver
The levels of TC, TG, LDL, and HDL were quantified using an automatic biochemical analyzer (TBA-40FR, Toshiba Medical Instruments, Tochigi-ken, Japan) following the manufacturer's guidelines. ALT, AST, and MDA levels were measured using test kits according to the provided instructions.
Histology analysis
For H&E staining, rat liver tissue is fixed in 4 % paraformaldehyde, washed, dehydrated through graded alcohols, cleared in xylene and paraffin, embedded, and sectioned at 3 μm. The sections are mounted, dried, dewaxed, and hydrated. Hematoxylin is applied for 5–10 min, differentiated with 0.1 % hydrochloric acid in ethanol, and blued with 1 % ammonia.
After eosin staining for 5–10 min, the sections are dehydrated, cleared in xylene, and sealed for microscopic examination of hepatocyte morphology.
For Oil Red O staining, fixed liver tissue is frozen, sectioned at 3 μm, and first stained with oil-red fixative for 30 s, followed by staining with Oil Red O solution for 8–10 min. Differentiation and hematoxylin staining are performed for 30 s, followed by anti-bluing in running water. Sections are sealed with glycerol gelatin and stored at 4 °C before scanning with an inverted fluorescence microscope.
Quantitative analysis of hepatic lipid accumulation was performed using ImageJ. The stained lipid-positive area (red-stained regions) was segmented and measured, and the percentage of lipid-positive area relative to total tissue area was calculated for each section. Three non-overlapping fields from three independent samples per group were analyzed, and the mean value was used for statistical comparison.
All microscopic images were captured at the same magnification (200 × ) using an optical microscope, with a scale bar of 100 μm shown in each image.
16S rRNA sequencing of microbial diversity analysis
Sequencing of rat cecum contents was performed using 16S rRNA high-throughput sequencing. After completion of genomic DNA extraction from fecal samples, it was analyzed by agarose gel electrophoresis with a mass fraction of 1 %. 338F (5′-ACTCCTACGGGGAGCAG-3′) and 806R (5′-GCACTACHVGGGGTWTCTAAT-3′) were used as the primers to amplify the designated regions of 16S (V3-V4), and the PCR amplification and sequencing were conducted by Shanghai Majorbio Biomedical Technology Co.
Ltd. using Illumina MiSeq PE300 platform according to the standard procedure. Fastp v0.19.6 was used for quality control, and FLASH software v1.2.11 was used for splicing the sample sequences. Raw data obtained from the Illumina MiSeq platform were specifically filtered using QIIME v1.9.1.
Non-repetitive sequences were analyzed by OTU (operational taxonomic units) clustering using UPARSE (version 7.0.1090 http://drive5.com/uparse/). Taxonomic classification of OTU representative sequences, defined by a 97 % similarity threshold, was then performed with the Bayesian algorithm of the RDP classifier (version 2.11 http://sourceforge.net/projects/rdp-classifier/). OTU statistics were performed using Usearch software v11.
Based on the OTU level, Mothur software was employed to analyze the Alpha diversity of rat gut microbiota within the samples. Principal coordinate analysis (PCoA) was then conducted to evaluate the Beta diversity among the samples and to analyze the compositional variability among rat gut microbiota. Functional annotation of 16S sequences was performed using PICRUSt v1.1.0.
Metabolomics analysis of cecal contents
A metabolomic analysis of rat feces was conducted using LC-MS. Following the extraction of metabolites from the samples, the data underwent LC-MS analysis for normalization. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were utilized as multivariate statistical methods to reflect metabolite differences among various groups.
Correlation and statistical analysis were subsequently performed on the identified differential metabolites. Moreover, functional analysis of these metabolites was conducted using the KEGG databases to investigate the regulatory effects of RGE on metabolic processes in hyperlipidemic rats.
Quantitative PCR (qPCR)
Quantitative PCR was conducted as described in our previously published research [12], with designed primers. The specific primers used were as follows: HMGCR (Forward)CAACCTCTACATCCGTCTCC, (Reverse) AGTGCTTTCTCCGTACCCT, GAPDH (Forward) TCTCTGCTCCTCCCTGTTC, (Reverse) ACACCGACCTTCACCATCT. The expression levels of HMGCR were normalized to GAPDH and calculated using the 2^–ΔΔCt method.
Western blot
The protocol of Western blot was described in our previous study [13], with modification in primary antibodies used for detection of PPARα (Santa Cruz, sc-398394, 1:1000 diluted), and SREBP-1c (Santa Cruz, sc-13551, 1:1000 diluted).
Statistical analysis
Statistical analysis of all data was performed using GraphPad Prism 8.4.3 software, with results presented as mean ± standard deviation (x‾ ± SD). The one-way ANOVA method was used. In 16S rRNA sequencing experiments, the Kruskal-Wallis test is used to compare samples from two or more sets of independent observations.
A P-value of <0.05 was considered statistically significant between groups.
Results
RGE regulates blood lipid levels in HFD-fed hyperlipidemia rats
To assess the effect of RGE on hyperlipidemia, Wistar rats were fed an HFD to induce hyperlipidemia and then treated with varying doses of RGE (125, 250, 500, and 1000 mg/kg/d) for 60 days (Fig. 1A). Blood lipid analysis revealed that HFD feeding significantly elevated serum TC, TG, and LDL levels, while HDL levels reduced in the M group (Fig.
1B–E). RGE treatment significantly reduced serum TC and TG levels in the 1000 mg/kg group. All RGE groups showed reductions in LDL levels and increases in HDL levels.
These results indicate that RGE effectively improves lipid profiles in hyperlipidemic rats.Fig. 1Effects of RGE administration for 60 days on lipid levels in HFD-fed rats (A) Experimental Design. (B–E) Serum Biochemical Markers: (B) TG, (C) TC, (D) HDL, and (E) LDL.
Data are presented as mean ± SD (n = 8). Statistical significance: vs. Group N: ###P < 0.001, ####P < 0.0001; vs.
Group M: ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001.Fig. 1
RGE alleviates HFD-induced liver injury and hepatic steatosis
To evaluate the effects of RGE on liver injury, H&E and Oil Red O staining were performed on liver tissue samples. In the N group, liver morphology was normal, with minimal fat content. In contrast, the M group showed hepatocyte enlargement, and a small number of hepatocytes displayed necrosis, accompanied by numerous fat vacuoles of varying sizes and severe lipid accumulation (Fig.
2A and B). Furthermore, imprinted vacuoles and balloon-like lipid droplets were also observed. Pathological changes in the RGE-treated groups were notably reduced, with improved liver structure, decreased steatosis, and fewer vacuoles and lipid droplets (Fig.
2C and D). Pathological scores from both H&E and Oil Red O staining (Fig. 2E and F) indicated significantly more severe liver damage in the M group, while RGE treatment significantly reduced these scores across all doses.Fig.
2RGE attenuates HFD-induced liver injury. (A) H&E staining (scale bar: 100 μm). (B) Oil Red O staining (scale bar: 100 μm).
(C–D) Quantification of H&E and Oil Red O-stained areas. (E–F) Pathological damage scores. (G) Body weight.
(H) Liver weight. (I) Liver coefficient. (J–K) Hepatic lipid content (TG and TC).
(L–M) Serum levels of ALT and AST. (N) Serum levels of MDA. Values are presented as mean ± SD (n = 8, pathological analysis n = 3).
Statistical significance: vs. Group N: ###P < 0.001, ####P < 0.0001; vs. Group M: ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, ∗∗∗∗P < 0.0001.Fig.
2
RGE treatment significantly attenuated HFD-induced increases in body weight, liver weight, and liver index (Fig. 2G–I). Correspondingly, RGE reduced intrahepatic lipid accumulation, as shown by decreased hepatic TC and TG levels (Fig.
2J and K). Additionally, RGE alleviated hepatocellular injury, evidenced by lower ALT and AST levels, and reduced oxidative stress, reflected by decreased MDA (Fig. 2L–N).
These findings, along with histopathological observations, collectively demonstrate that RGE effectively counteracts HFD-induced hepatic steatosis, mitigates liver injury, and reduces oxidative damage.
RGE modulates the gut microbiota composition in hyperlipidemia rats
To assess the effect of RGE on the gut microbiota in hyperlipidemic rats, gene sequencing was performed on cecum samples from the N, M, and RGE (1000 mg/kg) groups. In comparison to the N group, the M group exhibited reduced species diversity. RGE administration improved this diversity (Fig.
3A and B). In addition, complete separation of all three groups in Principal Coordinates Analysis (PCoA) demonstrates that RGE constructs a novel microbiota ecosystem rather than reverting to the pre-disease state (Fig. 3C).Fig.
3Effect of RGE on bacterial diversity and microbial composition in HFD rats. (A) Ace index; (B) Shannon diversity index among N, M, and RGH groups. (C) PCoA depicting microbiome variations among different groups; (D–F) Taxonomic composition and abundance distribution at phylum, family, and genus levels; (G–H) LEfSe multilevel species discriminant analysis and LDA effect values among the three groups.
(n = 5). ∗P < 0.05 represents a significant differential.Fig. 3
Changes in the gut microbiota of hyperlipidemic rats were compared at three taxonomic levels: phylum, family, and genus (Fig. 3D–F, respectively). At the phylum level, Firmicutes, Proteobacteria, and Bacteroidota were dominant.
The M group showed increased Firmicutes abundance, which decreased following RGE treatment. At the family level, Lachnospiraceae, Peptostreptococcaceae, and Erysipelotrichaceae were more abundant in the M group, while beneficial bacteria such as Muribaculaceae and Oscillospiraceae were decreased. RGE intervention reversed these trends, decreasing Lachnospiraceae, Peptostreptococcaceae, and Erysipelotrichaceae and increasing Muribaculaceae and Oscillospiraceae.
At the genus level, Romboutsia and Turicibacter were more abundant in the M group, with their levels decreased after RGE treatment. LEfSe multilevel species discriminant analysis revealed significant differences in species abundance across different taxonomic levels, including phylum, class, order, family, genus, and species. Notably, the N group had greater Bacteroidota abundance, while the M group showed pronounced differences at Bacilli species level, and Desulfovibrionaceae became notably enriched after RGE intervention.
(Fig. 3G and H). These findings suggest that RGE administration improves gut microbiota composition and mitigates the dysbiosis induced by an HFD in hyperlipidemic rats.
RGE affects fecal metabolite composition in HFD rats
Untargeted metabolomics analysis of cecal contents from the N, M, and RGE groups was performed using LC-MS. Correlation heat map analysis, PCA, and PLS-DA were used to assess metabolite differences. The correlation heat map (Fig.
4A) showed strong intra-group consistency and significant inter-group variation. PCA (Fig. 4B) revealed distinct clustering with 95 % confidence ellipses, while PLS-DA (Fig.
4C) highlighted significant metabolic changes due to both HFD intake and RGE treatment.Fig. 4Comparative and cluster analysis of differential metabolites in rats. (A) Sample Correlation Heatmap: The heatmap displays the correlation coefficient between samples, with colors indicating the strength of correlation.
A value closer to 1 reflects higher similarity. The clustered branches represent the relative distances between samples, with samples on the same branch showing greater similarity. (B) PCA and (C) PLS-DA Plots: Each point represents a sample, with the distance between points indicating the degree of similarity; closer points indicate greater similarity.
(D) Venn Diagram: Overlapping regions show metabolites common to multiple subgroups, while non-overlapping areas highlight metabolites specific to each subgroup. (E) Heatmap of the Top 50 Differential Metabolites: This heatmap illustrates the abundance of the top 50 metabolites that significantly differ between the N, M, and RGE groups. Metabolites are represented by rows, while columns correspond to the different groups.
Colors represent metabolite abundance, with red indicating increased levels and blue reflecting decreased levels. Darker shades signify higher abundance, while lighter shades indicate lower abundance. (n = 5).Fig.
4
Further analysis of differential metabolites identified 333 metabolites across the three groups (Fig. 4D). Cluster analysis of the top 50 metabolites (Fig.
4E) revealed clear differences in metabolite composition between the M and N groups, with RGE treatment partially reversing HFD-induced changes. These results emphasize the significant metabolic effects of RGE under HFD conditions.
RGE affects lipid metabolic pathways in HFD rats
To investigate the effect of RGE on the metabolites of gut microbiota, we identified 333 different metabolites, which were categorized using the KEGG compound classification. In this study, the most prominently regulated metabolites belonged to the lipid category, specifically fatty acyls and sterol lipids (Fig. 5A and B).Fig.
5KEGG analysis of differential metabolites, and Spearman correlation analysis of gut microbiota phyla and fecal metabolites. (A–B) KEGG Compound Classification Bar Charts: (A) Displays the classification of metabolites based on their biological effects. (B) Focuses specifically on lipid metabolites.
The vertical axis represents secondary classification categories, and the horizontal axis shows the number of metabolites assigned to each category. (C) KEGG Functional Pathway: A bar chart illustrating the distribution of different metabolites across various KEGG functional pathways. (D) KEGG Pathway Enrichment Bubble Plot: The horizontal axis represents the p-value for pathway enrichment, where smaller p-values indicate higher statistical significance (P < 0.05 denotes significant enrichment).
The vertical axis lists KEGG pathways enriched with differential metabolites. Bubble size corresponds to the degree of enrichment within each pathway. (E) This analysis shows the correlation between metabolites (listed on the right) and gut microbiota phyla (listed on the bottom).
Each section illustrates the correlation coefficient between a specific metabolite and bacterium. Color intensity reflects the strength of the correlation, with higher absolute values representing stronger associations. Significant correlations are marked by |r| ≥ 0.75 and P < 0.01 (∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001), emphasizing crucial relationships between specific microorganisms and metabolites.Fig.
5
KEGG pathway analysis revealed that RGE primarily influenced lipid metabolism pathways (Fig. 5C and D), with significant enrichment in Arachidonic acid and Sphingolipid metabolism, both key to lipid metabolism. These results suggest that RGE enhances lipid metabolic function and effectively regulates lipid levels.
Analysis of the correlation between gut microbiota and fecal differential metabolites
Spearman's correlation analysis was conducted to assess relationships between gut microbiota at the genus level (12 bacterial groups) and the top 50 significantly altered metabolites (VIP > 1.5) (Fig. 5E). A total of 112 meaningful relationships (|r| ≥ 0.75, P < 0.05) were identified.
Notable associations were found between specific phyla and fecal metabolites: Bacteroidota, Patescibacteria, Spirochaetota, and Firmicutes, which correlated with 42, 23, 16, and 24 metabolites, respectively. These results suggest that gut microbiota disturbances can significantly alter metabolomic profiles. Additionally, RGE treatment may help regulate microbiota, offering potential benefits for managing metabolism-related diseases and promoting gut homeostasis.
3.7 RGE modulates hepatic cholesterol and fatty acid metabolism related factors.
To further investigate the hepatic mechanisms through which RGE may influence lipid metabolism, the expression of critical regulatory factors related to cholesterol and fatty acid metabolism was examined. In comparison to the N group, the mRNA expression of HMG-CoA reductase (HMGCR) was notably lower in the M group, indicating impaired endogenous cholesterol synthesis (Fig. 6A).
Following RGE treatment, HMGCR expression was notably increased in the medium (500 mg/kg) and high-dose (1000 mg/kg) RGE groups, though levels remained lower than in the N group, suggesting a partial restoration of cholesterol biosynthesis.Fig. 6RGE regulates the expression of key lipid metabolism factors. (A) HMGCR mRNA expression in liver tissues.
(B) Western blot bands of hepatic PPARα and SREBP-1c. (C–D) Quantification analysis of PPARα and SREBP-1c protein expression normalized to GAPDH. Values are presented as mean ± SD (n = 8).
Statistical significance: vs. Group N: #P < 0.05, ####P < 0.0001; vs. Group M: ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001.Fig.
6
Western blot analysis revealed that the expression of peroxisome proliferator-activated receptor alpha (PPARα) protein was markedly downregulated in the M group, while all three RGE groups exhibited significant upregulation of PPARα (Fig. 6B–D). This indicates that RGE enhances hepatic fatty acid β-oxidation.
In contrast, the expression of sterol regulatory element-binding protein 1c (SREBP-1c), a key transcription factor that enhances fatty acid synthesis, was markedly elevated in the M group. Although RGE treatment reduced SREBP-1c protein expression in a dose-dependent manner, the differences were not statistically different from those in the M group. Collectively, these findings suggest that RGE modulates lipid metabolism by regulating both hepatic cholesterol metabolism and fatty acid oxidation, potentially improving lipid homeostasis in hyperlipidemic conditions.
Discussion
Hyperlipidemia, characterized by dysregulated cholesterol and triglyceride metabolism, poses significant risks for cardiovascular and hepatic diseases. While statins remain first-line therapeutics, their long-term use faces limitations due to adverse effects and incomplete efficacy. Previous studies have indicated that certain compounds in Korean Red Ginseng can improve lipid metabolism [[14], [15], [16]].
Our study reveals that Korean Red Ginseng extract (RGE) effectively ameliorates HFD-induced hyperlipidemia and associated hepatic injury in rats through a novel gut microbiota-liver metabolic axis, with coordinated modulation of lipid homeostasis. Crucially, we identify PPARα-mediated cholesterol excretion as the key mechanism underlying this effect.
In this study, a mixed hyperlipidemia rat model was developed by administering an HFD to the animals, which led to obesity and hepatic steatosis, marked by the accumulation of TC and TG [14]. The 8-week HFD feeding period has been established in prior research as sufficient for inducing significant hepatic steatosis in rodent models [17,18]. Additionally, excessive dietary cholesterol intake significantly increased LDL cholesterol levels in rats, elevating the risk of atherosclerosis [16].
Our results indicated that RGE partially mitigated HFD-induced body weight gain, maintained serum levels of TC and TG, and positively affected overall lipid profiles.
HFD consumption also contributed to liver tissue damage, including alterations in hepatocyte structure and function [15]. Serum hepatic aminotransferase activity (ALT and AST) is commonly used to assess liver injury [19]. RGE treatment reduced ALT and AST levels, alleviated MDA accumulation, and maintained lipid homeostasis.
Histological analysis of liver tissue sections, including H&E and oil red O staining, revealed that RGE reduced hepatic steatosis, hepatocellular fat infiltration, cellular swelling, and other pathological changes in the rats. These improvements suggest that RGE has a hepatoprotective effect and significantly enhances hepatic function.
In addition to its direct impact on lipid metabolism and liver function, our study investigated the involvement of gut microbiota in hyperlipidemia development [20,21]. Previous research has highlighted that Firmicutes bacteria promote energy absorption and weight gain, with an increased Firmicutes to Bacteroidetes (F/B) ratio linked to obesity in mice [22]. Similarly, in our study, obese rats exhibited increased body weight and Firmicutes abundance, along with a heightened F/B ratio, which was normalized following RGE intervention.
LC-MS untargeted metabolomics analysis identified notable differences in the metabolite compositions of intestinal contents across the various rat groups (N, M, and RGE 1000 mg/kg), with pathway analyses indicating RGE's primarily impact on lipid metabolic pathways. Significant correlations between gut microbial communities and metabolites were identified, underscoring the involvement of gut microbiota in the development of hyperlipidemia and its modulation by RGE.
Based on these integrated findings, we propose a putative gut microbiota-liver axis mechanism for RGE's action. RGE significantly remodeled gut microbiota composition, notably reducing the Firmicutes/Bacteroidetes ratio while enriching Muribaculaceae, Oscillospiraceae and Desulfovibrionaceae. These ecological changes were associated with increased production of microbial metabolites, particularly short-chain fatty acids (SCFAs), which are proposed to serve as signaling molecules that reach the liver to activate hepatic PPARα.
Other metabolites, including secondary bile acids, may also contribute to the modulation of lipid metabolism via receptors such as FXR and TGR5 [23].
To substantiate this proposed axis, we examined hepatic molecular regulators. A key finding was the significant upregulation of PPARα protein expression following RGE treatment. PPARα activation is suggested to promote fatty acid β-oxidation capacity, which may promote fatty acid catabolism and reduce triglyceride accumulation in the liver [24].
And it may also upregulate the rate-limiting enzyme for cholesterol conversion to bile acids, leading to a marked increase in cholesterol excretion and a significant decrease in intrahepatic cholesterol load [25]. This "excretion-centric" strategy contrasts with conventional inhibition-centric therapies. Concurrently, RGE treatment downregulated the expression of SREBP-1c, a key transcription factor involved in lipogenesis [18].
Although the protein reduction did not reach statistical significance, the concomitant and significant decrease in hepatic triglyceride content supports a functional suppression of this lipogenic pathway.
Furthermore, the mRNA expression of HMGCR, a crucial enzyme for cholesterol synthesis, was significantly suppressed in the HFD-fed model group. We propose that the pronounced hepatic cholesterol overload in our model triggered a compensatory feedback mechanism, reducing HMGCR activity to prevent further cholesterol synthesis. This regulatory feedback aligns with previous studies showing that hepatic cholesterol accumulation suppresses HMGCR transcription [26,27].
Upon RGE treatment, HMGCR expression was partially restored, with significant increases observed in both the medium and high-dose RGE groups. This restoration suggests that RGE alleviated the hepatic cholesterol burden, relieving the pathological suppression of cholesterol synthesis and allowing biosynthesis to return to a basal, homeostatic level, rather than an overactive synthetic response. Therefore, the elevated HMGCR levels reflect a re-establishment of basal synthetic capacity as the pathological suppression is alleviated.
Beyond these direct metabolic regulations, RGE-mediated improvements in gut microbial ecology and SCFAs production may foster an environment that favors intestinal barrier integrity [28]. An improved barrier may reduce the leakage of pro-inflammatory molecules, thereby mitigating the activation of systemic inflammatory pathways, such as TLR4/NF-κB [29], which is a known contributor to hepatic metabolic dysfunction [30]. While this represents a plausible indirect mechanism, its validation requires future investigation.
However, there are still some limitations in our research. First, the use of a whole Korean Red Ginseng extract, while reflective of its traditional use and holistic action, precludes the identification of the specific bioactive components responsible for the observed effects. Isolating and validating the roles of these key constituents is an important goal for future research.
Second, although our multi-omics approach revealed strong associations between gut microbiota remodeling, metabolite shifts, and hepatic metabolic improvements, the causal relationships within this gut-liver axis remain to be fully established. Interventions such as fecal microbiota transplantation or metabolite supplementation are required to confirm whether specific microbial derivatives directly mediate the regulation of hepatic genes such as PPARα and SREBP-1c. Finally, the downregulation of HMGCR in the hyperlipidemic model group diverges from some previous reports but supports the presence of a sterol-overload-induced feedback mechanism.
However, the precise conditions that trigger this form of HMGCR suppression warrant further investigation.
Regarding safety, the 60-day treatment period in this study was designed to evaluate the lipid-lowering efficacy of RGE and does not constitute a full chronic toxicity assessment. However, Red Ginseng has a long history of safe use as a food and supplement. More directly, its safety over extended periods is supported by a 24-week clinical trial [31], and a 12-month chronic toxicity study in rats [32], which substantially mitigate general safety concerns for its medium-to long-term applications.
Future studies specifically designed for chronic toxicity assessment at the highest effective doses used in this model could further solidify these findings.
Despite these limitations, our study offers a systematic and valuable framework for understanding how RGE mediates metabolic improvements through the gut-liver axis. It provides a solid foundation for future research and the development of ginseng-based therapies for metabolic diseases.
Data availability statement
All data will be made available on request. Data for this article are available in the NCBI repository (Accession number: PRJNA1263126).
Author's contributions
Yannan Zheng conducted experimental studies, data acquisition and analysis, statistical analysis, literature search, and contributed to manuscript preparation and editing. Mingyue Lv, Errui Zhang, Min Zheng, and Trinh Thach Thi Nguyen were involved in experimental studies, data acquisition and analysis, and statistical analysis. Hongxi Xu, Man Yuan, Shijie Wan and Weiliang Gu contributed to intellectual content's concept, design, and definition and reviewing the manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.