Multi-omics analysis reveals gut microbiota remodeling and lipid metabolism regulation during the treatment of nonalcoholic fatty liver disease with Yindan Pinggan capsule

Multi-omics analysis reveals gut microbiota remodeling and lipid metabolism regulation during the treatment of nonalcoholic fatty liver disease with Yindan Pinggan capsule | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multi-omics analysis reveals gut microbiota remodeling and lipid metabolism regulation during the treatment of nonalcoholic fatty liver disease with Yindan Pinggan capsule Jinli Hou, Sen Li, Fengrong Zhang, Honghe Xiao, Xianyu Li, Hongjun Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7631502/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Chinese Medicine → Version 1 posted 7 You are reading this latest preprint version Abstract Background Nonalcoholic fatty liver disease (NAFLD) is a common chronic liver disorder with limited treatment options. Yindan Pinggan capsule (YDPG), a traditional Chinese medicine, has demonstrated potential in managing liver diseases, yet its efficacy and mechanisms in NAFLD remain unclear. Methods A high-fat diet (HFD)-induced NAFLD mouse model was established. The major bioactive components of YDPG, including baicalin, geniposide, and glycyrrhizic acid, were quantified using UPLC-QQQ-MS/MS. Integrated 16S rRNA sequencing and serum metabolomics were employed to analyze gut microbiota and metabolic profiles. qPCR was used to assess gene expression related to lipid metabolism. Results YDPG treatment significantly reduced body weight, liver index, hepatic lipid accumulation, inflammation, and improved serum lipid profiles and liver function (AST/ALT). It reshaped gut microbiota by decreasing harmful genera (e.g., Clostridioides , Ileibacterium ) and enriching beneficial ones (e.g., Dubosiella ), while regulating key metabolites involving bile acids, short-chain fatty acids, and neurotransmitters. qPCR confirmed modulation of lipid metabolism genes (e.g., Pparg, Cyp7a1, Hmgcr). Conclusions YDPG alleviates NAFLD by modulating the gut-liver axis, restoring gut microbial balance, and correcting metabolic disorders, demonstrating its potential as a multi-target therapeutic agent for NAFLD. Yindan Pinggan capsule Nonalcoholic fatty liver disease Lipid metabolism Gut microbiota Untargeted metabolomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Nonalcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver condition globally, affecting over 29% of the world's population [ 1 ]. As NAFLD progresses, it can evolve into more severe forms, including steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma [ 2 , 3 ]. Beyond its hepatic manifestations, NAFLD is intricately linked with major extrahepatic complications, including cardiovascular disease [ 4 ], type 2 diabetes [ 5 ], chronic kidney disease [ 6 ], and hypertension [ 7 ]. The etiology of NAFLD is complex and multifactorial, involving dysregulated lipid metabolism, insulin resistance, and chronic inflammation. Crucially, emerging evidence highlights the gut-liver axis as a central player in NAFLD pathogenesis. Intestinal dysbiosis disrupts gut barrier integrity, increases endotoxin translocation (e.g., lipopolysaccharide), and alters microbial metabolite production (e.g., short-chain fatty acids and bile acids). These changes directly promote hepatic lipid accumulation (steatosis), inflammation, and insulin resistance, driving NAFLD development and progression [ 8 , 9 ]. Concurrently, impaired hepatic fatty acid oxidation, enhanced de novo lipogenesis, and defective very-low-density lipoprotein secretion are key metabolic disturbances underpinning hepatic steatosis and lipotoxicity [ 10 , 11 ]. Current therapeutic options for NAFLD remain limited. While lifestyle modification is foundational, pharmacotherapy is often necessary. In 2024, Rezdiffra (resmetirom) became the first and only drug approved by the FDA specifically for nonalcoholic steatohepatitis, though it is contraindicated in patients with decompensated cirrhosis. Commonly reported adverse effects include diarrhea, nausea, pruritus, abdominal pain, vomiting, constipation, and dizziness; notably, diarrhea and nausea often emerge early in treatment and are typically mild to moderate [ 12 ]. In contrast, Yindan Pinggan capsule (YDPG), a traditional Chinese formulation, offers a complementary approach. Given the pivotal role of gut-liver axis in NAFLD pathogenesis as described above, YDPG's multi-herb composition may provide a holistic strategy by potentially modulating gut microbiota, restoring intestinal barrier integrity, and ameliorating metabolic disturbances. Its historical application in various liver diseases suggests favorable tolerability. However, it still lacks robust clinical trial validation and regulatory approval specifically for NAFLD/NASH in most countries. Its precise mechanism of action, particularly concerning lipid metabolism and gut microbiome interaction, remains less elucidated than Rezdiffra’s targeted THR-β agonism, and potential herb-drug interactions require careful consideration. Other agents such as vitamin E [ 13 ], pioglitazone [ 14 ], and metformin [ 15 ] also show variable efficacy and/or safety concerns in clinical trials [ 16 ], underscoring the ongoing need for more effective, well-tolerated, and mechanism-based treatments. YDPG is a proprietary traditional Chinese medicine produced by Zhangzhou Pien Tze Huang Pharmaceutical Co., Ltd. Its composition includes a blend of herbal elements: Yin Chen Hao (YCH, Artemisia capillaris Thunb.), Long Dan (LD, Gentiana scabra Bunge), Huang Qin (HQ, Scutellaria baicalensis Georgi), Zhi Zi (ZZ, Gardenia jasminoides J.Ellis), Bai Shao (BS, Paeonia lactiflora Pall.), Dang Gui (DG, Angelica sinensis (Oliv.) Diels), Zhu Dan Fen (ZDF, Sus scrofa domestica Brisson), and Gan Cao (GC, Glycyrrhiza uralensis Fisch.). The current research has two main aspects. The determination of Chlorogenic acid, ferulic acid, geniposide, ammonium glycyrrhetinic acid, gentiopicroside, baicalin lays the foundation for establishing the YDPG quality standard [ 17 ]. Contrarily, YDPG is often utilized to deal with liver diseases, showing good efficacy and safety in treating alcoholic liver disease, chronic hepatitis, alcoholic liver fibrosis and other diseases [ 17 ]. However, its potential therapeutic effects on NAFLD, particularly concerning the modulation of gut microbiota and lipid metabolism, have not been investigated. Given the established roles of gut dysbiosis and metabolic dysfunction in NAFLD, and the historical use of YDPG constituents in liver disorders, we hypothesize that YDPG may ameliorate NAFLD by targeting these pathways. In this study, we first characterized the major constituents of YDPG using UPLC-QQQ-MS/MS. We then employed a high-fat diet (HFD)-induced NAFLD mouse model to evaluate the therapeutic effects of YDPG. To elucidate the potential mechanisms, we integrated 16S rRNA gene sequencing of the gut microbiota with serum untargeted metabolomics analysis. Furthermore, we validated the expression of key genes involved in hepatic fatty acid metabolism using quantitative real-time PCR (qPCR). 2. Materials and methods 2.1. Materials and chemicals This research employed YDPG, manufactured by Zhangzhou Pien Tze Huang Pharmaceutical Co., Ltd, based in Zhangzhou, Fujian, China. HPLC grade acetonitrile (CH 3 CN) and methanol were procured from Merck KGaA (Merck KGaA, Darmstad, Germany). Additionally, formic acid (FA) was procured from Aladdin Reagent (Shanghai) Co., Ltd (Aladdin, Shanghai, China). Baicalin (purity ≥ 98%), geniposide (purity ≥ 98%), paeoniflorin (purity ≥ 98%), liquiritin (purity ≥ 98%), glycyrrhizic acid (purity ≥ 98%), and gentiopicroside (purity ≥ 98%) were provided by chengdu must bio-technology co.,LTD(chengdu, china). Hematoxylin & eosin (H&E) and Oil Red O stains were obtained from Solarbio Science & Technology Co., Ltd. (Solarbio, Beijing, China). The assessments of ALT, AST, TG, TC, HDL-C, and LDL-C utilized reagents sourced from Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu, China). 2.2. YDPG Sample Preparation In the 2020 version of The Pharmacopoeia of the People's Republic of China, baicalin is specified as a crucial component for quality control in YDPG. In addition, geniposide, paeoniflorin, liquiritin, glycyrrhizic acid, gentiopicroside were used as quality control components. The mother liquor concentration of the standard was 5 mg/ml, and the detected concentration on the machine was 0.5 µg/ml. Furthermore, an ultrasonic extraction was performed by adding 129 mg of YDPG to a 4 ml solution consisting of 75% methanol. This extraction lasted for 60 min at a temperature of 25℃. Following this, the mixture underwent centrifugation at a speed of 13,000 rpm for a duration of 10 min. The liquid fraction obtained was diluted 100 times to prepare the YDPG test solution. 2.3. Identification of YDPG compounds by UPLC-QQQ-MS/MS For the quality control evaluation of YDPG, an advanced analytical method known as UPLC-QQQ-MS/MS (Waters, Milford, MA, USA) alongside the Waters UPLC HSS T3 column (2.1 × 100 mm, 1.8 µm) was utilized. The parameters set for the analysis included a flow rate of 3 µl/min, a column thermal condition of 45℃, and a sample injection amount 2 µl. The UPLC's solvent system comprises 0.1% formic acid in water (A) and acetonitrile (B). The elution gradient profile was established in the following manner: from 0 to 1 min, maintaining 15% B; from 1 to 6 min, increasing from 15% to 70% B; from 6 to 7 min, increasing to 100% B; maintaining 100% B from 7 to 9 min; quickly reducing to 15% B from 9 to 9.1 min; and holding at 15% B from 9.1 to 11 min. The mass spectrometry settings included the following parameters: positive and negative electrospray ionization (ESI + and ESI – ) with the capillary voltage adjusted to 3.0 kV; the temperature for the dehydration gas set to 500℃; a nitrogen cone gas flow rate of 10 L/h; and a dehydration nitrogen gas flow of 1000 L/h. The content of geniposide, paeoniflorin, liquiritin, baicalin, glycyrrhizic acid, gentiopicroside in YDPG were calculated by the external standard 1-point method. Information management was executed utilizing the MassLynx 4.1 and Target Lynx software (Waters). 2.4. Animal experiment 2.4.1. Construction of NAFLD model in vivo In this study, 75 male C57BL/6 mice (averaging 19.2 ± 1.2g) were gained from Sibeifu Biotechnology Co., Ltd. (Beijing, China) and were meticulously maintained in the specific pathogen-free (SPF) animal facility of the Institute of Basic Research in Traditional Chinese Medicine, Chinese Academy of Traditional Chinese Medicine. The procurement of these mice was in strict adherence to the guidelines provided under Animal License No. SCXK (Beijing) 2019-0010. C57BL/6 mice were randomly split into two categories: the control category (Ctrl, n = 15) and the HFD category (n = 46). Ctrl category were fed a common diet and had free reign with water. On the other hand, the HFD category mice received a high-fat feed consisting of 45% dietary fat. We measured the weights of the mice on a weekly basis. After eight weeks, six Ctrl mice and twelve HFD mice were selected for ophthalmic tissue retrieval and blood sampling (Fig. 2 A). The objective of this procedure was to verify the successful creation of the NAFLD model by assessing four blood lipid parameters. 2.4.2. Grouping and administration The human YDPG capsule dose is thrice a day, two capsules every time, six capsules daily (3 g/day). The dose of YDPG-M in mice was 3 g (70 kg) × 0.0026/0.02 (conversion coefficient between humans and mice) = 0.39 g/kg/day. Half and twice the dosage of YDPG were defined as YDPG-L and YDPG-H. (YDPG-H: 0.39 g/kg × 2 = 0.78 g/kg/day; YDPG-L: 0.302 g/kg × 1/2 = 0.195 g/kg/day). Ctrl group (n = 9). The HFD group was divided into HFD group (n = 9) and a treatment groups: HFD + YDPG-L (0.195 g/kg, n = 9), HFD + YDPG-M (0.39 g/kg, n = 9), and HFD + YDPG-H (0.78 g/kg, n = 9). The Ctrl group received a standard diet, whereas the HFD and YDPG groups were provided with a diet consisting of 45% high-fat content. The YDPG was administered daily by gavage based on body weight, with a gavage volume of 0.15 mL/10 g. The Ctrl and HFD groups were given physiological saline by gavage. Other groups of mice were given corresponding concentrations of YDPG by gavage every day for four consecutive weeks. All animal experiments approved by the Ethical Committee of Experimental Animal Welfare of the Experimental Research Center, China Academy of Chinese Medicine Science (approval ID: ERCCACMS21-2307-02). 2.4.3. Harvesting experimental samples The mice were weighed twice weekly and recorded weight changes for four weeks. The final weighing was on day 28. Initially, blood samples were obtained by collecting the eyeballs, succeeded by centrifugation at 3,500 rpm and 4℃ for 10 min, after which the samples were packaged individually. Then, the cecal contents were taken in a sterile environment, after long-term cryopreservation at -80℃. Then, the mice's liver and White adipose tissue were weighed to calculate the liver and white adipose indices. Certain liver specimens were subjected to Oil Red O and H&E staining procedures. Remaining livers were stored in a low-temperature refrigerator at -80℃ for the next experiment. 2.5. HE Staining Liver tissues were fixed in 4% paraformaldehyde at room temperature for 48 h, embedded in paraffin, and sectioned at 4 µm. The sections were stained with hematoxylin and eosin (H&E staining kit, Solarbio, Beijing, China) and examined under a light microscope (×20) for pathological assessment. 2.6. Oil Red O Staining Liver tissues were fixed in 4% paraformaldehyde at room temperature for 48 h and then cryosectioned. The frozen sections were stained with Oil Red O for 10 min, differentiated in 60% isopropanol, and rinsed in distilled water. Subsequently, all sections were counterstained with hematoxylin for 2 min, washed in pure water, differentiated in 60% alcohol for 6 s, and rinsed again. Finally, sections were covered with glycerol gelatin. Histological structure and lipid deposition in mouse liver were observed under a microscope. 2.7. Biochemical Analysis A biochemical analyzer was employed automatically to measure four blood lipid parameters (TC, TG, LDL-C, HDL-C), along with AST and ALT levels in serum, for the assessment of abnormal blood lipids and liver damage. 2.8. 16S rRNA sequencing Genomic DNA was isolated from the sample using the CTAB technique, with the purity of the DNA subsequently evaluated via agarose gel electrophoresis. A suitable quantity of sample DNA was transferred into a centrifuge tube (1 ng/µl). We performed PCR amplification by utilizing diluted genomic DNA as the template. We employed New England Biolabs' Phase® PCR Master Mix having high fidelity and GC Buffer, along with an efficient high-fidelity enzyme. Additionally, we utilized V3/V4 primers with Barcode for the amplification process (341F: CCTAYGGGRBGCASCAG; 806R: GGACTACNNGGGTATCTAAT). The detection of PCR products involved electrophoresis, utilizing agarose gel at a concentration of 2%. Subsequently, magnetic bead purification was conducted on PCR products that successfully met the criteria set by the test. For library construction, they utilized the TruSeq® DNA PCR-Free Sample Preparation Kit. The resulting library was then evaluated for quantity using Qubit and qPCR techniques. Once the library passed the quality control, sequencing was carried out using the NovaSeq6000 machine. The Uparse algorithm (obtained from USARCH v7 software, available at http://www.drive5.com/Uparse/ ) must be employed. It clusters every Effective Tag from all samples with a 97% level of consistency regarding their identity. Mothur method and SILVA138.1 ( http://www.arb-silva.de/ ) were used to annotate and analyze species (threshold range of 0.8-1). R package, GraphPad Prism 8.0 and other software were utilized to examine the diversity of microbial composition in the samples. 2.9. Linkage analysis of gut microbiota and metabolites The correlation between differential microorganisms in cecal contents and differential metabolites in serum Ctrl and HFD groups was examined using Spearman correlation analysis. Metabolite and microbial data were imported with significant differences into the Tutools platform ( http://www.cloudtutu.com ). Spearman correlation analysis was conducted, and the correlation coefficient was converted into a thermogram to display the correlation between different metabolites and gut microbiota visually. 2.10. LC-MS analysis of the serum The ice blocks in the sample were completely melted after removing it from the − 80°C refrigerator and thawing it on ice. Then, 50 µl of the sample was transferred into the numbered centrifuge tube that corresponded to it. Subsequently, 300 µl of a 20% acetonitrile and methanol solution, acting as the internal standard for extraction, was added. This mixture was then subjected to vigorous vortexing for 3 min, succeeded by centrifugation at 12,000 rpm at a temperature of 4°C for 15 min. Subsequently, 200 µl of the supernatant was carefully moved to a distinct tube for centrifugation at -20°C for half an hour. Next, a second centrifugation at 12,000 rpm for 3 min was performed at 4°C. In the final step, 180 µl of the surface liquid was transferred into the inner liner of a vial for subsequent analysis using a spectrometer. The UPLC analysis was performed utilizing a Shimadzu LC-30A system (Japan) with a BEH C18 chromatography column (2.1×100 mm, 1.8 µm; Waters, MA, USA). The operating parameters included a flow rate of 4 µl/min, a column temperature of 40°C, and a sample injection volume of 2 µl. The mobile phase for the UPLC system consisted of 0.1% formic acid in water (A) and acetonitrile (B). A gradient elution method was employed for the separation process: starting with 5% B at 0 min, increasing from 5% to 90% B over 11 min, maintaining at 90% B from 11 to 12 min, decreasing back to 5% B from 12 to 12.1 min, and then holding at 5% B from 12.1 to 14 min. The electric spray ionization (ESI) source-equipped SCIEX TripleTOF 6600 + located in Foster City, CA, USA was utilized to acquire the mass spectrum data at ESI + and ESI − scanning modes. The parameter settings for the operation were: gas temperature 550℃ (ESI + ) and 450℃ (ESI − ); Ion Source Gas 50 psi; Capillary voltage 5500 V (ESI + ) and − 4500V (ESI − ); Declustering Potential 60 V. The mass interval was set from 50 to 1000 Da. 2.11. Data processing Proteo Wizard was utilized for the conversion of raw data into the mzXML format. The XCMS software was then used for peak extraction, followed by adjustments to align the retention times. The "SVR" method adjusted the peak area and filtered out peaks with a missing rate greater than 50% and an RSD larger than 30% in each sample group. The metabolites identified and calibrated were obtained by conducting searches in both the laboratory's self-constructed databases and publicly available databases. 2.12. qPCR Total RNA was extracted from liver tissue utilizing the Total RNA Isolation Kit V2 (Vazyme, China). The qPCR method system: 40 cycles of PCR, each cycle comprising denaturation at 95°C for 10 s and annealing at 60°C for 30 s. A dissolving curve analysis concluded the procedure, progressively thermal treatment from 60 to 95°C at a rate of 0.05°C/s. The 2 -ΔΔCt method was applied in this research. Please consult Table S7 for the detailed primer sequences employed in this research. 2.13. Statistical analysis The statistical analyses and the creation of histograms were conducted utilizing GraphPad Prism (8.0, San Diego, USA). Results were presented as the average accompanied by the standard deviation. The examination of variance within groups for differences was executed utilizing ANOVA, supplemented by Tukey's post hoc and t-tests (P < 0.05). For PCoA and Rank abundance curve analyses, R software (Version 4.1.2) was used. For LEfSe analysis, the LEfSe software was used with a filtering value of 4 for the default LDA score. Additionally, the adoption of PCA was carried out using R (base package, version 3.5.1), while R (MetaboAnalystR, version 1.0.1) was utilized for OPLS-DA. 3. Results 3.1. UPLC-QQQ-MS/MS examination of the primary bioactive compounds in YDPG To serve as reference standards, Baicalin, geniposide, paeoniflorin, liquiritin, glycyrrhizic acid, and gentiopicroside were utilized as quality control components. The reference standards and typical total ion chromatograms [ 18 ] of YDPG were shown in Fig. 1 . Table 1 indicated the analysis results of UPLC-QQQ-MS/MS for YDPG. The quality control compounds in YDPG were identified as gentiopicroside in G. scabra, g eniposide in G. jasminoides , liquiritin and glycyrrhizic acid in G. uralensis , baicalin in S. baicalensis , paeoniflori in P. lactiflora . The levels of specific compounds in YDPG were analyzed and found to be 10.056 mg/g of gentiopicroside, 13.113 mg/g of geniposide, 0.798 mg/g of liquiritin, 3.009 mg/g of glycyrrhizic acid, 6.576 mg/g of baicalin, and 8.771 mg/g of paeoniflori. Table 1 Results of UPLC-QQQ-MS/MS analysis of YDPG. NO. Compound Formula Adducts MRM RT Peak Area (reference standards) Peak Area (sample) Content (mg/g) 1 Geniposide C 17 H 24 O 10 M-H 387.10 > 225.10 1.65 8562.031 56135.746 13.112 2 Paeoniflorin C 23 H 28 O 11 M + FA − 525.45 > 449.51 2.31 718055.875 3149042.250 8.771 3 Liquiritin C 21 H 22 O 9 M-H 417.36 > 255.39 2.77 2879280.250 1149247.375 0.798 4 Baicalin C 21 H 18 O 11 M-H 445.03 > 269. 12 3.47 2706863.750 8899785.000 6.576 5 Glycyrrhizic acid C 42 H 62 O 16 M-H 821.79 > 351.49 4.71 143790.766 216338.984 3.009 6 Gentiopicroside C 16 H 20 O 9 M + H 2 O 374.10 > 195.20 2.56 11831.761 59492.000 10.056 3.2. YDPG reduced body weight, liver index, and white fat index in HFD-induced NAFLD The weight of HFD rats increased with an increase in feeding time, as demonstrated in Figure S1 A. After an eight-week period, the HFD category's weight was substantially greater than that of the Ctrl group, as depicted in Figure S1 B. Blood samples taken from the Ctrl and model groups were then centrifuged (3,000 rpm, 4℃, 10 min), and the supernatant was analyzed to measure four blood lipid parameters. The HFD category displayed significantly higher levels of lipid parameters compared to the Ctrl category, as depicted in Figure S1 C-F. These results indicate that a high-fat diet has a notable impact on lipid levels in the body. The Ctrl and HFD groups were given physiological saline, and the drug groups was given different concentrations of drug solutions (HFD + YDPG-L (0.151 g/kg), HFD + YDPG-M (0.302 g/kg), and HFD + YDPG-H (0.604 g/kg) for 28 days to test the therapeutic impact of YDPG. Throughout the treatment timeframe, mice body weight in each group was registered in correlation with their increasing feeding duration. The weight of the Ctrl group mice remained stable, while the model group mice gained weight and exhibited depilation on their backs and tails. However, the weight of the treated group mice first decreased and then remained at a certain level, and there was no depilation on their backs and tails (Fig. 2 B-C). Extended ingestion of a HFD can potentially cause elevated liver and white fat indexes. In this study, the liver index in the HFD category was substantially greater contrasted with the Ctrl category. In contrast, the YDPG administration group could remarkably reduce the liver index, with the best mid dose efficacy (Fig. 2 D). The Ctrl group exhibited a smaller index of white fat in the epididymal region contrasted with the model category. The YDPG administration group diminished mice's liver index without a dose-dependent relationship (Fig. 2 E). The results indicate that YDPG significantly reduces body weight, liver index, and epididymal white fat index in HFD group. 3.3. YDPG decreased liver tissue damage and inflammation in HFD-induced NAFLD The HE staining method can effectively observe liver tissue's structure and pathological changes. In this study, the HFD had disordered liver tissue structure, uneven liver cell arrangement, enlarged liver nuclei, and hepatic steatosis (vacuolar structure). In the YDPG treatment group, the overall disorder of liver tissue structure was alleviated, resulting in a notable reduction in the level of hepatic steatosis (vacuolar structure). The size of the nuclei in the HFD + YDPG-M group was notably smaller (Fig. 3 A). AST and ALT are important indicators for evaluating liver tissue damage. The findings of this investigation demonstrate significantly elevated levels of AST and ALT in HFD group contrast to the Ctrl group. After YDPG administration, the AST and ALT levels were significantly reduced. Among them, HFD + YDPG-L and HFD + YDPG-M could significantly reduce the AST, while HFD + YDPG-M could significantly reduce the ALT (Fig. 3 D). Therefore, the HFD + YDPG-M had a better therapeutic effect on liver damage in HFD prompted NAFLD. To assess the inflammatory reaction due to liver damage further, qPCR was utilized to quantify the quantities of TNF-α, IL-6, and IL-1β in liver tissue. The results showed a notable elevation in the mRNA levels of TNF-α, IL-1β, and IL-6 in the HFD group relative to the Ctrl group. Nevertheless, the expression degrees of these genes were markedly decreased in the YDPG-treated groups. Specifically, the HFD + YDPG-M treatment remarkably reduced the IL-1β mRNA level, while HFD + YDPG-L effectively reduced TNF-α and IL-6 mRNA levels (Fig. 3 E). 3.4. YDPG lipid accumulation and four items of blood lipid in HFD-induced NAFLD The Oil Red O staining results clearly showed minimal lipid droplet aggregation in the control group, whereas a significant accumulation of lipid droplets was observed in the high-fat diet group. This indicates that the high-fat diet caused an increase in lipid droplet formation, highlighting the impact of diet on lipid metabolism. The YDPG groups exhibited a notable reduction in liver fat droplets and an improvement in steatosis. Notably, the administration of HFD + YDPG-L substantially diminished the accumulation of liver fat droplets (Fig. 3 B). The degrees of the four blood lipid parameters were substantially rose in the HFD category in comparison to the Ctrl group. Alternatively, the YDPG treatment group significantly inhibit four blood lipid items levels. The administration of HFD + YDPG-M proved to be more effective in reducing the four blood lipid levels (Fig. 3 C). For LDL-C indicators, there was a dose-dependent tolerance of YDPG drugs. The findings suggest that YDPG could potentially offer protective benefits against steatosis in mouse livers. 3.5. YDPG regulated configuration of gut microbiota in HFD-prompted NAFLD Since gut microbiota composition is intimately connected to NAFLD, we used 16S rRNA sequencing to compare gut microbiota proportions in HFD groups and YDPG groups. The Rank Abundance curve, which illustrates both species abundance and evenness, was utilized for data interpretation. The curve range of Ctrl, HFD, HFD + YDPG-L, and HFD + YDPG-M was larger; the species wind was higher. The curve was relatively flat, and the species distribution was uniform compared to HFD + YDPG-H (Fig. 4 A). This study analyzed 8864 fecal microbiota sequencing readings, among which the average readings for the Ctrl, HFD, HFD + YDPG-L, HFD + YDPG-M, and HFD + YDPG-H groups were 1884, 1834, 1787, 1905, and 1454, respectively. Further, the OUT results of each group were displayed through an upset, with points and lines representing whether there were common OUTs between the groups. Among them, there are 673 outputs shared by Ctrl, HFD, HFD + YDPG-L, HFD + YDPG-M, and HFD + YDPG-H, while there are 64 outputs shared by Ctrl and YDPG groups (Fig. 4 B). Based on the PCoA diagram (Fig. 4 C), a clear distinction was observed between the Ctrl group compared to both the YDPG groups and the HFD group in relation to the phylum level. Firmicutes showed a notable rise while Fusobacteria and Bacteroidetes exhibited a decrease within the HFD group. The YDPG treatment, however, managed to restore the levels of Firmicutes , Fusobacteria , and Bacteroidetes to a certain degree. The Firmicutes to Bacteroidetes ratio, associated with metabolic diseases, was substantially rose in the HFD category. On the contrary, the HFD + YDPG-L and HFD + YDPG-M groups showed decreased ratios compared to the HFD group, with the HFD + YDPG-M group displaying a significantly lower ratio (Fig. 4 E). Figure 4 F depicts the bacterial community's genus-level abundance. The HFD group experienced substantial increases in Clostridioides , Ileibacterium , Allobaculum , and Enterococcus (P < 0.05). In the HFD category, Dubosiella was identified to be less abundant. However, the categories treated with HFD + YDPG-L and HFD + YDPG-M recorded significant declines in the abundances of Clostridioides , Ileibacterium , and Enterococcus . Furthermore, Allobaculum 's presence notably diminished in both the HFD + YDPG-L and HFD + YDPG-H groups. On the other hand, Dubosella saw a considerable increase in abundance in the HFD + YDPG-M and HFD + YDPG-H groups (Fig. 4 G). Further analysis using LEfSe indicated a high pervasiveness of Clostridioides at the genus level in the HFD category. Conversely, at the genus level, the pervasiveness of Aerococcus , Colidextribacter , and Lysinibacillus was increased by YDPG-L treatment, whereas YDPG-H treatment raised the levels of Akkermansia , Ileibacterium , and Staphylococcus (Fig. 5 ). 3.6. YDPG affected the distribution of serum metabolites in HFD-induced NAFLD The composition of gut microbiota also impacts metabolic products, thereby influencing the occurrence and progression of various diseases. In this investigation, alterations in serum metabolites were explored using non-targeted metabolomics. First, perform PCA and OPLS-DA analysis on Ctrl, HFD, HFD + YDPG-L, HFD + YDPG-M, and HFD + YDPG-H samples. The PCA results showed that Ctrl, HFD, and HFD + YDPG were significantly separated (Fig. 6 A). The OPLS-DA results indicated that Ctrl, HFD, HFD + YDPG-M, and HFD + YDPG-H can be distinguished. However, HFD + YDPG-M, HFD + YDPG-H, and HFD + YDPG-L cannot be remarkably distinguished (Fig. 6 B). Further, R²X, R²Y and Q² were evaluated the reliability of the OPLS-DA model. The Q2 = 0.801, R²Y = 0.94, and P < 0.05, which implied that the system had the best predictive ability (Fig. 6 C). A screening method was utilized to pinpoint differential metabolites distinguishing the Ctrl from the HFD groups (FC ≥ 2 or ≤ 0.5, VIP ≥ 1 and P < 0.05) (Figure S2 ). This analysis uncovered that 125 differential metabolites experienced an increase, while 228 were decreased (Fig. 6 D). Specifically, there were 109, 166, and 132 differential metabolites identified when comparing the HFD group to the HFD + YDPG-L, HFD + YDPG-M, and HFD + YDPG-H groups, respectively (Fig. 6 E). 3.7. YDPG alleviated HFD-induced NAFLD by regulating short-chain fatty acids, bile acids, and neurotransmitters By analyzing the cecum contents, we have determined that YDPG can regulate the abundance of Clostridioides , Ileibacterium , Allobaculum , Enterococcus , and Dubosella . Research has found that Clostridioides , Ileibacterium , Allobaculum , Enterococcus , and Dubosella can affect the production of bile acids, neurotransmitters, and short-chain fatty acids. The interaction among these compounds plays a crucial role in the onset and advancement of NAFLD. Therefore, we conducted an in-depth analysis of the differential metabolites, like short-chain fatty acids, bile acids, and neurotransmitters. Within the HFD group, a number of significant disparities were noted. In particular, variations in 19 short-chain fatty acids, 22 bile acids, and two neurotransmitters were significant (Fig. 6 F). The pathways enriched in these differential metabolites include linoleic acid metabolism, ovarian steroidogenesis, cortisol synthesis and secretion, primary bile acid biosynthesis, steroid hormone biosynthesis, bile secret, cAMP signaling pathway, and PPAR signaling pathway (Fig. 6 G). Moreover, the significantly different TOP signaling pathways are tightly linked to lipid metabolism, indicating that YDPG regulates lipid metabolism in treating NAFLD. The metabolites highlighted within the red box in Fig. 6 F show significant variations across the Ctrl, HFD, and YDPG groups. Among these, indole-3-propionic acid, 3-(methylthio)propionic acid, and valeric acid are distinct short-chain fatty acids. Notably, the HFD group exhibited a significant rise in 3-(methylthio)propionic acid and valeric acid levels, while these levels were substantially diminished in the HFD + YDPG-L group. Indole-3-propionic acid exhibited a substantial diminishment in the HFD category, but its content remarkably increased in the HFD + YDPG-H group, as depicted in Fig. 7 A. Nordeoxycholic acid, glycolic acid, tauoursodeoxycholic acid, and Taurochenodeoxycholic acid were identified as notable bile acids with significant differences. The HFD group demonstrated a significant elevation in glycolic acid, taurosodeoxycholic acid, taurocholic acid, and taurochenodeoxycholic acid, while their content decreased significantly in the HFD + YDPG-L group. Conversely, nordeoxycholic acid experienced a significant decrease in the HFD group, but its content increased remarkably in the HFD + YDPG-H group, as depicted in Fig. 7 B. Serotonin, a neurotransmitter with differential expression, showed a remarkable reduction in the HFD group, while its content markedly increased in the HFD + YDPG-M group, as shown in Fig. 7 C. 3.8. Results of the correlation analysis between gut microbiota and metabolites The correlation between differential microorganisms in cecal contents and differential metabolites in serum Ctrl and HFD groups was examined using Spearman correlation analysis. Glycolic acid, taurosodeoxycholic acid, taurocholic acid, and taurochenodeoxycholic acid were remarkably favorably linked with Clostridioides , Illeiberium , and Enterococcus . The taurochenodeoxycholic acid, valeric acid, and 3- (Methylthio) propionic acid components significantly correlated with Allobaculum . A notable negative linkage exists between serotonin and nordeoxycholic acid components with Allobaculum . Additionally, serotonin demonstrates a significant negative correlation with Clostridioides , Ileiberium , and Enterococcus . Lastly, the indole-3-propionic acid component exhibits a significant negative correlation with Ileibacterium and Enterococcus . A significant relationship exists between differential endophytic bacteria and the metabolites modulated by YDPG, as depicted in Fig. 8 . 3.9. YDPG-regulated lipid metabolism in HFD-induced NAFLD The pathways enriched in these differential metabolites include linoleic acid metabolism, ovarian steroidogenesis, cortisol synthesis and secretion, primary bille acid biosynthesis, steroid hormone biosynthesis, cAMP signaling pathway, and PPAR signaling pathway. Therefore, we further validated the gene mRNA expression level of YDPG-regulated lipid metabolism-related signaling pathways through qPCR and further elucidated the mechanism of YDPG efficacy. Within the primary bile acid biosynthesis pathway, the expression degrees of Cyp39a1, and Hsd3b7 were noticeably elevated in HFD group. However, the administration of YDPG effectively decreased the expression levels of these enzymes (Fig. 9 A). HFD boosted Pparg mRNA expression in the PPAR signaling pathway. Still, the YDPG treatment group severely decreased it. In HFD group, the level of Ppard mRNA expression showed a notable decrease, which was observed to significantly increase upon YDPG administration (Fig. 9 B). Moreover, the HFD group also exhibited remarkable enhancements in the mRNA expression degrees of Cyp7a1, LXRa (Nr1h3), and Hmgcr in the cholesterol metabolism pathway. Conversely, the YDPG-treated group showed decreased gene expression compared to the HFD group (Fig. 9 C). Cyp7a1 can regulate cholesterol metabolism and primary bile acid biosynthesis. In the HFD groups, Abcg5, Abcg8, Nfkb1, Gadl1, Fmo1 and Scd1 expression levels were observed to be markedly elevated, while the YDPG treatment group significantly inhibited gene expression (Fig. 9 D- 9 G). Cyp8b1, Cyp27a1, Srebp2 and Acsl1 expression degrees were insignificant in the Ctrl, HFD, and YDPG categories (Figure S3 ). To summarize, YDPG has the potential to enhance HFD-induced NAFLD by modulating the signaling pathway of PPAR, metabolism of cholesterol, secretion of bile, taurine and hydrogen metabolism, and other pathways associated with lipid metabolism. 4. Discussion Fatty liver includes alcoholic fatty liver and NAFLD. Excessive and prolonged intake of alcohol leads to the progression of alcoholic fatty liver. Moreover, this condition possesses the potential to evolve into alcoholic hepatitis, liver fibrosis, and cirrhosis. When there is no alcohol consumption, fat accumulates abnormally in the liver to form NAFLD. It is related to metabolic syndrome, obesity, hypertension, hyperlipidemia, diabetes and other metabolic diseases. Previous studies have shown that YDPG improves acute alcoholic hepatitis by inhibiting inflammatory responses and oxidative stress levels [ 17 ]. However, the effect of YDPG on NAFLD has not been reported. This research results indicated that YDPG exerts protective effects against HFD-induced NAFLD in mice, ameliorating key pathological features including body weight gain, hepatic steatosis, liver injury, inflammation, and dyslipidemia. More importantly, our integrated approach combining pharmacochemical analysis, gut microbiota profiling, and serum metabolomics reveals that the therapeutic mechanism of YDPG is multi-targeting and holistic, primarily mediated through the remodeling of gut microbiota structure and the subsequent regulation of microbial-host co-metabolites, including short-chain fatty acids, bile acids, and neurotransmitters, which ultimately modulates hepatic lipid metabolic pathways [ 19 , 20 ]. The most salient therapeutic advantage of YDPG, particularly when contrasted with current single-target pharmacotherapies like the THR-β agonist Rezdiffra (resmetirom), lies in its multi-component, multi-pathway holistic regulatory strategy. Our UPLC-QQQ-MS/MS analysis confirmed that YDPG contains a spectrum of bioactive compounds, such as baicalin, geniposide, glycyrrhizic acid, and paeoniflorin, which are known to possess anti-inflammatory, antioxidant, and lipid-modulating properties [ 21 – 24 ]. This phytochemical complexity allows YDPG to synchronously target the core axes of NAFLD pathogenesis—gut dysbiosis, metabolic disturbance, and inflammation—a feat difficult to achieve with a single synthetic molecule. While Rezdiffra offers a targeted, potent agonism of THR-β, its application is restricted to non-cirrhotic patients and is associated with a notable incidence of gastrointestinal adverse effects [ 12 ]. In contrast, YDPG exhibited remarkable tolerability in our model, aligning with its historical use in various liver conditions, and it produced therapeutic effects across a wide range of metabolic and inflammatory parameters without observed adverse events. However, since Rezdiffra is currently unavailable through official channels in China, we did not conduct a direct experimental comparison in our study. A key mechanistic insight from this study is the pivotal role of gut-liver axis modulation in YDPG's efficacy. The HFD-induced dysbiosis, characterized by an elevated Firmicutes/Bacteroidetes ratio and a bloom of pro-inflammatory genera like Clostridioides , Ileibacterium , and Enterococcus , was significantly reversed by YDPG treatment. Concomitantly, YDPG promoted the abundance of beneficial genera like Dubosiella and Akkermansia , which are associated with improved gut barrier integrity and metabolic health [ 25 , 26 ]. This microbiota remodeling directly translated into a normalization of critical microbial-derived metabolites. YDPG administration significantly rectified the imbalances in serum levels of bile acids (e.g., taurocholic acid, taurochenodeoxycholic acid), short-chain fatty acids (e.g., valeric acid, indole-3-propionic acid), and the neurotransmitter serotonin. The strong correlations between these differential metabolites and the altered gut microbes, as revealed by Spearman analysis, solidify the premise that YDPG acts first on the gut ecosystem, with systemic metabolic consequences. Short-chain fatty acid (SCFA), including acetic acid (AA), propionic acid (PA), and butyric acid (BA) [ 27 ], is a key bridge connecting intestinal microbiota and the body, which can enrich beneficial bacteria, inhibit harmful bacteria, and affect intestinal health and systemic metabolism. PA can attenuate steatohepatitis by inhibiting endotoxin leakage [ 28 ]. BA alleviated lipid formation and inflammation [ 29 ]. In untargeted metabolomics data analysis, two propionic acids differed significantly between YDPG and HFD. The content of Indole-3-propionic acid displayed a significant reduction within the HFD group. Conversely, the YDPG group exhibited a notable ability to increase its concentration, enhance the abundance of advantageous microorganisms, and suppress the proliferation of detrimental bacteria. Furthermore, it was found that the HFD group remarkably increased valeric acid content. However, after YDPG administration, the valeric acid content was significantly inhibited. Among them, 3- (Methylthio) propionic acid and valeric acid content were positively linked to the concentration of Clostridioides , Ileibacterium , Allobaculum , and Enterococcus . Nonetheless, there's a significant negative linkage between indole-3-propionic acid and the abundance of Ileibacterium and Enterococcus . Bile acids are an important bile component produced by cholesterol metabolism. Bile acids play diverse roles in the human body, encompassing synthesis and absorption of cholesterol [ 30 ], antibacterial effects [ 31 ], liver metabolism [ 32 ] and impact on the occurrence and progression of NAFLD [ 18 ]. Five different bile acids were were pinpointed in this study between the YDPG and HFD categories. Among them, the concentrations of Taurocholic acid, Taurosodeoxycholic acid, Glycolic acid, and Taurochenodeoxycholic acid remarkably elevated in the HFD category and significantly diminished after administration of YDPG. Subsequently, the differences between the HFD and TDPG groups of Nordeoxycholic acid were opposite to the results of the other four bile acids. Taurocholic acid, Taurosodeoxycholic acid, Glycocholic acid, Taurochenodeoxycholic acid, and Nordeoxycholic acid were remarkably favorably linked to the concentration of Clostridioides , Illeiberium , Allobaculum , and Enterocus . The decline in the content of bile acids was in line with the diminishment in the concentration and variety of gut microbiota. Results suggested that YDPG elicited favorable outcomes for the host through the regulation of the structure and metabolites of intestinal microorganisms. Furthermore, the downstream hepatic effects of this gut-centric action were elucidated through gene expression analysis. YDPG effectively downregulated the expression of key genes driving lipogenesis (Srebp2, Scd1), cholesterol synthesis (Hmgcr), and bile acid synthesis (Cyp7a1, Cyp39a1), while modulating pivotal nuclear receptors (Pparg, Ppard, LXRa) involved in lipid homeostasis. This coordinated suppression of anabolic pathways, coupled with a potential enhancement of fatty acid oxidation (suggested by elevated Ppard), underpins the observed alleviation of hepatic steatosis and lipotoxicity [ 33 – 36 ]. The fact that these metabolic benefits were achieved without a strict dose-dependent relationship suggests a homeostatic-restoring effect characteristic of multi-target botanical drugs, wherein a moderate dose (HFD + YDPG-M) often achieved optimal efficacy by balancing synergistic actions without triggering compensatory mechanisms. 5. Conclusion In conclusion, our findings provide robust preclinical evidence that YDPG is a promising multi-target therapeutic agent for NAFLD. Its unique strength stems from a systems-level approach that concurrently ameliorates gut microbiota dysbiosis, corrects deranged microbial metabolism, and dampens hepatic inflammatory and metabolic stress, thereby addressing the intricate pathophysiology of NAFLD more comprehensively than current single-target options. This study not only validates the traditional use of YDPG but also propels it into the modern therapeutic landscape as a gut-microbiota-focused regulator of metabolic health. Future clinical trials are warranted to translate these promising findings into a validated treatment strategy for NAFLD patients. Declarations Acknowledgements Not applicable. Author contributions JH: Validation, Investigation, Visualization, Writing—original draft, Writing—review and editing, Data curation. SL: Validation, Investigation, Visualization, Writing—review and editing, Validation. FZ: Investigation, Methodology, Visualization. HX: Investigation, Methodology. XL: Conceptualization, Project administration, Supervision, Writing–review and editing. HY: Conceptualization, Project administration, Supervision, Writing—review and editing. Funding This research was supported by the Scientific and technological innovation project of China Academy of Chinese Medical Sciences (CI2021B017), Scientific and Technological Innovation Project of CACMS (CI2021A05032), National Natural Science Foundation of China (82174238), and the Fundamental Research Funds for the Central Public Welfare Research Institutes (RXRC2022002, ZZ13-YQ-080, and XTCX2021001). Data availability The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (PRJCA045435) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. Shared URL: https://ngdc.cncb.ac.cn/gsa/s/T24aF4Jk. Declaration of Interest Statement 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. 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Supplementary Files SupplementaryTableS5.xlsx SupplementaryTableS6.xlsx SupplementaryTableS7.docx SupplementaryTableS4.xlsx SupplementaryTableS2.xlsx SupplementaryTableS1.xlsx SupplementaryTableS3.xlsx FigureS2.tif FigureS1.tif FigureS3.tif Cite Share Download PDF Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Chinese Medicine → Version 1 posted Editorial decision: Revision requested 23 Oct, 2025 Reviews received at journal 29 Sep, 2025 Reviewers agreed at journal 29 Sep, 2025 Reviewers invited by journal 29 Sep, 2025 Editor assigned by journal 25 Sep, 2025 Submission checks completed at journal 25 Sep, 2025 First submitted to journal 16 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":67162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe chemical compositions of YDPG were characterized using UPLC-QQQ-MS/MS.\u003c/strong\u003eIn negative (\u003cstrong\u003eA\u003c/strong\u003e) and positive (\u003cstrong\u003eC\u003c/strong\u003e) ion modes, the profiles of YDPG are presented. The standards in negative ion mode (\u003cstrong\u003eB\u003c/strong\u003e) include 1: geniposide; 2: paeoniflorin; 3: liquiritin; 4: baicalin; 5: glycyrrhizic acid. Additionally, the standard for gentiopicroside (6) is shown in positive ion mode (\u003cstrong\u003eD\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/7f9e438c318012d2f6edb956.jpg"},{"id":93599627,"identity":"ef7cd5ce-adb6-4fb3-beab-2267bd3b5f8f","added_by":"auto","created_at":"2025-10-15 14:36:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":281028,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYDPG reduced body weight, liver index, and white fat index in HFD-induced NAFLD.\u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Schematic diagram of animal experimental design. (\u003cstrong\u003eB\u003c/strong\u003e)The curve of mice body weight change within 16 weeks. (\u003cstrong\u003eC\u003c/strong\u003e) The mice body weight on day 29. (\u003cstrong\u003eD\u003c/strong\u003e) Liver index. (\u003cstrong\u003eE\u003c/strong\u003e) White adipose index. ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/fe9850e2f15cc8538bdd5dd0.jpg"},{"id":93600578,"identity":"3051720d-5012-48d5-80b6-e136b418aea2","added_by":"auto","created_at":"2025-10-15 14:44:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":418201,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYDPG mitigated HFD-induced NAFLD\u003c/strong\u003e, as evidenced by (\u003cstrong\u003eA\u003c/strong\u003e) HE staining outcomes of liver tissue (magnification 20×), (\u003cstrong\u003eB\u003c/strong\u003e) Oil Red O staining results (magnification 20×), (\u003cstrong\u003eC\u003c/strong\u003e) levels of four blood lipid parameters in serum, (\u003cstrong\u003eD\u003c/strong\u003e) serum AST and ALT levels, and (\u003cstrong\u003eE\u003c/strong\u003e) qPCR analysis of TNF-α, IL-6, and IL-1β in liver tissue. Statistical significance is denoted as *p\u0026lt;0.05, **p\u0026lt;0.01, and ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/ca17eb7c38885d342e217e3a.jpg"},{"id":93599694,"identity":"273c9017-6936-40b0-a720-628ac7b4220f","added_by":"auto","created_at":"2025-10-15 14:36:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":269212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYDPG controlled the gut microbiota in HFD-induced NAFLD.\u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Rank abundance curve. (\u003cstrong\u003eB\u003c/strong\u003e) Upset plot analysis between OTU number and Ctrl, HFD, HFD+YDPG-L, HFD+YDPG-M, HFD+YDPG-H categories. (\u003cstrong\u003eC\u003c/strong\u003e) PCoA examination of every group. (\u003cstrong\u003eD\u003c/strong\u003e) The occurrence at the phylum level in the bacterial community. (\u003cstrong\u003eE\u003c/strong\u003e) Differential microbiota at the taxonomic level of phylum. (\u003cstrong\u003eF\u003c/strong\u003e) The occurrence at the genus level in the bacterial community. (\u003cstrong\u003eG\u003c/strong\u003e) Differential microbiota at genus level in HFD-induced NAFLD. *p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/93d5a50c39df36279024ae07.jpg"},{"id":93599624,"identity":"f63f8b51-0b07-4e7e-b32d-258478f3ecde","added_by":"auto","created_at":"2025-10-15 14:36:27","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":219299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut microbiota analysis using LEfSe in the control group (Ctrl), high-fat diet group (HFD), low dose YDPG group (HFD+YDPG-L), medium dose YDPG group (HFD+YDPG-M), and high dose YDPG group (HFD+YDPG-H).\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Phylogenetic tree showcasing the variations in gut microbiota from the phylum to genus level. (\u003cstrong\u003eB\u003c/strong\u003e) Bar graph displaying discriminatory power measured by LDA score. The threshold for the LDA score exceeded 4.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/6b0811126e5a799bc38b39e8.jpg"},{"id":93599832,"identity":"e5a76b04-c5fb-4089-af0a-12061c7e9cf8","added_by":"auto","created_at":"2025-10-15 14:38:32","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":297330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYDPG affected the distribution of serum metabolites in HAD-prompted NAFLD. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) PCA analysis result. (\u003cstrong\u003eB\u003c/strong\u003e) OPLS-DA analysis result. (\u003cstrong\u003eC\u003c/strong\u003e) Conducting 200 random permutation to gauge the accuracy of the OPLS-DA model. (\u003cstrong\u003eD\u003c/strong\u003e) Volcano chart illustrating differential metabolites between the Ctrl group and the HFD group. (\u003cstrong\u003eE\u003c/strong\u003e)Venn plot showing the overlap of metabolites in different groups. (\u003cstrong\u003eF\u003c/strong\u003e) Differences in short-chain fatty acids, bile acids, and neurotransmitters observed between the HFD and Ctrl groups. (\u003cstrong\u003eG\u003c/strong\u003e) Identifying enriched pathways related to differential short-chain fatty acids, bile acids, and neurotransmitters.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/f734bb4cb4fb74d4c764bab1.jpg"},{"id":93599715,"identity":"8e747a82-8f67-485c-b999-d81e25056815","added_by":"auto","created_at":"2025-10-15 14:37:07","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":231540,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe 9 biomarkers in Ctrl, HFD and YDPG groups.\u003c/strong\u003e*p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/e5c87977b1d94bec1deb2578.jpg"},{"id":93599610,"identity":"1eb96594-677f-47b2-8ce4-081c4e12c567","added_by":"auto","created_at":"2025-10-15 14:36:18","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":32514,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation assessment between differential metabolites and gut microbiota. \u003c/strong\u003eSignificance levels: *p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/b09d933e15e8983b94cb402b.jpg"},{"id":93599721,"identity":"f67e6d70-9ba0-4540-9f38-474a97d0a06a","added_by":"auto","created_at":"2025-10-15 14:37:15","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":201755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYDPG-regulated lipid metabolism in HFD-induced NAFLD. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Cyp39a1 and Hsd3b7 in the main bile acid synthesis pathway. (\u003cstrong\u003eB\u003c/strong\u003e) Ppard and Pparg in the PPAR signaling pathway. (\u003cstrong\u003eC\u003c/strong\u003e) Cyp7a1, LXRa (Nr1h3), and Hmgcr in cholesterol metabolism pathway. (\u003cstrong\u003eD\u003c/strong\u003e) Abcg5, Abcg8 mRNA in the bile secretion. (\u003cstrong\u003eE\u003c/strong\u003e) The expression degree Nfkb1 mRNA. (\u003cstrong\u003eF\u003c/strong\u003e) Gadl1, Fmo1 in the taurine and hypotaurine metabolism. (\u003cstrong\u003eG\u003c/strong\u003e) The expression level Scd1 mRNA in the biosynthesis of unsaturated fatty acids. *p\u0026lt;0.05; **p\u0026lt;0.01; ***p\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/839b424898fc16abe10b81ae.jpg"},{"id":105755591,"identity":"83137707-9a0c-4a6e-b02a-58829c1effad","added_by":"auto","created_at":"2026-03-30 16:28:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3492808,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/975e6f61-4054-47e7-b8cb-9319494319f2.pdf"},{"id":93599743,"identity":"84f0387a-2e01-49c6-9b8d-8eb2c55a2a28","added_by":"auto","created_at":"2025-10-15 14:37:31","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27144,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/4a2a39efe1654480e333fdd4.xlsx"},{"id":93599606,"identity":"43e7f013-6c02-4ef0-a335-a4d7cdb1b8fb","added_by":"auto","created_at":"2025-10-15 14:36:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13858,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/e39313d38ec9cde258341ba4.xlsx"},{"id":93599718,"identity":"38832576-70b3-47ca-8bee-f6c1376582ef","added_by":"auto","created_at":"2025-10-15 14:37:10","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14420,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS7.docx","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/45aaf4799166793c0f307db0.docx"},{"id":93599882,"identity":"df290d5f-3d16-4add-ad2b-150a6e2c64be","added_by":"auto","created_at":"2025-10-15 14:39:20","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":59260,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/b8b8095fab947e58a4c38ec8.xlsx"},{"id":93599697,"identity":"334bcb36-71b1-4f41-8ba7-e2bdff8a23cc","added_by":"auto","created_at":"2025-10-15 14:36:45","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":43309,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/91c0e13982d901b23bbc8d86.xlsx"},{"id":93599861,"identity":"dd83b8ec-f6ef-4fa5-bb3e-815bf45ee957","added_by":"auto","created_at":"2025-10-15 14:38:59","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":118363,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/74a616861abe8180a00afe3d.xlsx"},{"id":93599725,"identity":"62e6a37f-956a-4378-8870-4c2c110d9d4b","added_by":"auto","created_at":"2025-10-15 14:37:20","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":70500,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/913b5454d055727869e02267.xlsx"},{"id":93599616,"identity":"3ab4d368-945e-43e3-85cb-af09f24fa80c","added_by":"auto","created_at":"2025-10-15 14:36:24","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":686770,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/27e1b9ef1abba33606461c4c.tif"},{"id":93599724,"identity":"97427987-898f-43e5-82af-6bc74ec7459e","added_by":"auto","created_at":"2025-10-15 14:37:20","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":4127076,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/3ef953ac8560d696a30bfece.tif"},{"id":93599684,"identity":"70b9890e-345f-45c1-940e-c348141c3634","added_by":"auto","created_at":"2025-10-15 14:36:33","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1130358,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7631502/v1/989c117362f6a76dbf213e56.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics analysis reveals gut microbiota remodeling and lipid metabolism regulation during the treatment of nonalcoholic fatty liver disease with Yindan Pinggan capsule","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNonalcoholic fatty liver disease (NAFLD) has emerged as the most prevalent chronic liver condition globally, affecting over 29% of the world's population [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As NAFLD progresses, it can evolve into more severe forms, including steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Beyond its hepatic manifestations, NAFLD is intricately linked with major extrahepatic complications, including cardiovascular disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], type 2 diabetes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], chronic kidney disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and hypertension [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe etiology of NAFLD is complex and multifactorial, involving dysregulated lipid metabolism, insulin resistance, and chronic inflammation. Crucially, emerging evidence highlights the gut-liver axis as a central player in NAFLD pathogenesis. Intestinal dysbiosis disrupts gut barrier integrity, increases endotoxin translocation (e.g., lipopolysaccharide), and alters microbial metabolite production (e.g., short-chain fatty acids and bile acids). These changes directly promote hepatic lipid accumulation (steatosis), inflammation, and insulin resistance, driving NAFLD development and progression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Concurrently, impaired hepatic fatty acid oxidation, enhanced de novo lipogenesis, and defective very-low-density lipoprotein secretion are key metabolic disturbances underpinning hepatic steatosis and lipotoxicity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrent therapeutic options for NAFLD remain limited. While lifestyle modification is foundational, pharmacotherapy is often necessary. In 2024, Rezdiffra (resmetirom) became the first and only drug approved by the FDA specifically for nonalcoholic steatohepatitis, though it is contraindicated in patients with decompensated cirrhosis. Commonly reported adverse effects include diarrhea, nausea, pruritus, abdominal pain, vomiting, constipation, and dizziness; notably, diarrhea and nausea often emerge early in treatment and are typically mild to moderate [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In contrast, Yindan Pinggan capsule (YDPG), a traditional Chinese formulation, offers a complementary approach. Given the pivotal role of gut-liver axis in NAFLD pathogenesis as described above, YDPG's multi-herb composition may provide a holistic strategy by potentially modulating gut microbiota, restoring intestinal barrier integrity, and ameliorating metabolic disturbances. Its historical application in various liver diseases suggests favorable tolerability. However, it still lacks robust clinical trial validation and regulatory approval specifically for NAFLD/NASH in most countries. Its precise mechanism of action, particularly concerning lipid metabolism and gut microbiome interaction, remains less elucidated than Rezdiffra\u0026rsquo;s targeted THR-β agonism, and potential herb-drug interactions require careful consideration. Other agents such as vitamin E [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], pioglitazone [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and metformin [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] also show variable efficacy and/or safety concerns in clinical trials [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], underscoring the ongoing need for more effective, well-tolerated, and mechanism-based treatments.\u003c/p\u003e\u003cp\u003eYDPG is a proprietary traditional Chinese medicine produced by Zhangzhou Pien Tze Huang Pharmaceutical Co., Ltd. Its composition includes a blend of herbal elements: Yin Chen Hao (YCH, \u003cem\u003eArtemisia capillaris\u003c/em\u003e Thunb.), Long Dan (LD, \u003cem\u003eGentiana scabra\u003c/em\u003e Bunge), Huang Qin (HQ, \u003cem\u003eScutellaria baicalensis\u003c/em\u003e Georgi), Zhi Zi (ZZ, \u003cem\u003eGardenia jasminoides\u003c/em\u003e J.Ellis), Bai Shao (BS, \u003cem\u003ePaeonia lactiflora\u003c/em\u003e Pall.), Dang Gui (DG, \u003cem\u003eAngelica sinensis\u003c/em\u003e (Oliv.) Diels), Zhu Dan Fen (ZDF, \u003cem\u003eSus scrofa domestica\u003c/em\u003e Brisson), and Gan Cao (GC, \u003cem\u003eGlycyrrhiza uralensis\u003c/em\u003e Fisch.). The current research has two main aspects. The determination of Chlorogenic acid, ferulic acid, geniposide, ammonium glycyrrhetinic acid, gentiopicroside, baicalin lays the foundation for establishing the YDPG quality standard [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Contrarily, YDPG is often utilized to deal with liver diseases, showing good efficacy and safety in treating alcoholic liver disease, chronic hepatitis, alcoholic liver fibrosis and other diseases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, its potential therapeutic effects on NAFLD, particularly concerning the modulation of gut microbiota and lipid metabolism, have not been investigated.\u003c/p\u003e\u003cp\u003eGiven the established roles of gut dysbiosis and metabolic dysfunction in NAFLD, and the historical use of YDPG constituents in liver disorders, we hypothesize that YDPG may ameliorate NAFLD by targeting these pathways. In this study, we first characterized the major constituents of YDPG using UPLC-QQQ-MS/MS. We then employed a high-fat diet (HFD)-induced NAFLD mouse model to evaluate the therapeutic effects of YDPG. To elucidate the potential mechanisms, we integrated 16S rRNA gene sequencing of the gut microbiota with serum untargeted metabolomics analysis. Furthermore, we validated the expression of key genes involved in hepatic fatty acid metabolism using quantitative real-time PCR (qPCR).\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Materials and chemicals\u003c/h2\u003e\u003cp\u003eThis research employed YDPG, manufactured by Zhangzhou Pien Tze Huang Pharmaceutical Co., Ltd, based in Zhangzhou, Fujian, China. HPLC grade acetonitrile (CH\u003csub\u003e3\u003c/sub\u003eCN) and methanol were procured from Merck KGaA (Merck KGaA, Darmstad, Germany). Additionally, formic acid (FA) was procured from Aladdin Reagent (Shanghai) Co., Ltd (Aladdin, Shanghai, China). Baicalin (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%), geniposide (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%), paeoniflorin (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%), liquiritin (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%), glycyrrhizic acid (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%), and gentiopicroside (purity\u0026thinsp;\u0026ge;\u0026thinsp;98%) were provided by chengdu must bio-technology co.,LTD(chengdu, china). Hematoxylin \u0026amp; eosin (H\u0026amp;E) and Oil Red O stains were obtained from Solarbio Science \u0026amp; Technology Co., Ltd. (Solarbio, Beijing, China). The assessments of ALT, AST, TG, TC, HDL-C, and LDL-C utilized reagents sourced from Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu, China).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. YDPG Sample Preparation\u003c/h2\u003e\u003cp\u003eIn the 2020 version of The Pharmacopoeia of the People's Republic of China, baicalin is specified as a crucial component for quality control in YDPG. In addition, geniposide, paeoniflorin, liquiritin, glycyrrhizic acid, gentiopicroside were used as quality control components. The mother liquor concentration of the standard was 5 mg/ml, and the detected concentration on the machine was 0.5 \u0026micro;g/ml. Furthermore, an ultrasonic extraction was performed by adding 129 mg of YDPG to a 4 ml solution consisting of 75% methanol. This extraction lasted for 60 min at a temperature of 25℃. Following this, the mixture underwent centrifugation at a speed of 13,000 rpm for a duration of 10 min. The liquid fraction obtained was diluted 100 times to prepare the YDPG test solution.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Identification of YDPG compounds by UPLC-QQQ-MS/MS\u003c/h2\u003e\u003cp\u003eFor the quality control evaluation of YDPG, an advanced analytical method known as UPLC-QQQ-MS/MS (Waters, Milford, MA, USA) alongside the Waters UPLC HSS T3 column (2.1 \u0026times; 100 mm, 1.8 \u0026micro;m) was utilized. The parameters set for the analysis included a flow rate of 3 \u0026micro;l/min, a column thermal condition of 45℃, and a sample injection amount 2 \u0026micro;l. The UPLC's solvent system comprises 0.1% formic acid in water (A) and acetonitrile (B). The elution gradient profile was established in the following manner: from 0 to 1 min, maintaining 15% B; from 1 to 6 min, increasing from 15% to 70% B; from 6 to 7 min, increasing to 100% B; maintaining 100% B from 7 to 9 min; quickly reducing to 15% B from 9 to 9.1 min; and holding at 15% B from 9.1 to 11 min.\u003c/p\u003e\u003cp\u003eThe mass spectrometry settings included the following parameters: positive and negative electrospray ionization (ESI\u003csup\u003e+\u003c/sup\u003e and ESI\u003csup\u003e\u0026ndash;\u003c/sup\u003e) with the capillary voltage adjusted to 3.0 kV; the temperature for the dehydration gas set to 500℃; a nitrogen cone gas flow rate of 10 L/h; and a dehydration nitrogen gas flow of 1000 L/h. The content of geniposide, paeoniflorin, liquiritin, baicalin, glycyrrhizic acid, gentiopicroside in YDPG were calculated by the external standard 1-point method. Information management was executed utilizing the MassLynx 4.1 and Target Lynx software (Waters).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Animal experiment\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. Construction of NAFLD model in vivo\u003c/h2\u003e\u003cp\u003eIn this study, 75 male C57BL/6 mice (averaging 19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2g) were gained from Sibeifu Biotechnology Co., Ltd. (Beijing, China) and were meticulously maintained in the specific pathogen-free (SPF) animal facility of the Institute of Basic Research in Traditional Chinese Medicine, Chinese Academy of Traditional Chinese Medicine. The procurement of these mice was in strict adherence to the guidelines provided under Animal License No. SCXK (Beijing) 2019-0010. C57BL/6 mice were randomly split into two categories: the control category (Ctrl, n\u0026thinsp;=\u0026thinsp;15) and the HFD category (n\u0026thinsp;=\u0026thinsp;46). Ctrl category were fed a common diet and had free reign with water. On the other hand, the HFD category mice received a high-fat feed consisting of 45% dietary fat. We measured the weights of the mice on a weekly basis. After eight weeks, six Ctrl mice and twelve HFD mice were selected for ophthalmic tissue retrieval and blood sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The objective of this procedure was to verify the successful creation of the NAFLD model by assessing four blood lipid parameters.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. Grouping and administration\u003c/h2\u003e\u003cp\u003eThe human YDPG capsule dose is thrice a day, two capsules every time, six capsules daily (3 g/day). The dose of YDPG-M in mice was 3 g (70 kg) \u0026times; 0.0026/0.02 (conversion coefficient between humans and mice)\u0026thinsp;=\u0026thinsp;0.39 g/kg/day. Half and twice the dosage of YDPG were defined as YDPG-L and YDPG-H. (YDPG-H: 0.39 g/kg \u0026times; 2\u0026thinsp;=\u0026thinsp;0.78 g/kg/day; YDPG-L: 0.302 g/kg \u0026times; 1/2\u0026thinsp;=\u0026thinsp;0.195 g/kg/day).\u003c/p\u003e\u003cp\u003eCtrl group (n\u0026thinsp;=\u0026thinsp;9). The HFD group was divided into HFD group (n\u0026thinsp;=\u0026thinsp;9) and a treatment groups: HFD\u0026thinsp;+\u0026thinsp;YDPG-L (0.195 g/kg, n\u0026thinsp;=\u0026thinsp;9), HFD\u0026thinsp;+\u0026thinsp;YDPG-M (0.39 g/kg, n\u0026thinsp;=\u0026thinsp;9), and HFD\u0026thinsp;+\u0026thinsp;YDPG-H (0.78 g/kg, n\u0026thinsp;=\u0026thinsp;9). The Ctrl group received a standard diet, whereas the HFD and YDPG groups were provided with a diet consisting of 45% high-fat content. The YDPG was administered daily by gavage based on body weight, with a gavage volume of 0.15 mL/10 g. The Ctrl and HFD groups were given physiological saline by gavage. Other groups of mice were given corresponding concentrations of YDPG by gavage every day for four consecutive weeks. All animal experiments approved by the Ethical Committee of Experimental Animal Welfare of the Experimental Research Center, China Academy of Chinese Medicine Science (approval ID: ERCCACMS21-2307-02).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3. Harvesting experimental samples\u003c/h2\u003e\u003cp\u003eThe mice were weighed twice weekly and recorded weight changes for four weeks. The final weighing was on day 28. Initially, blood samples were obtained by collecting the eyeballs, succeeded by centrifugation at 3,500 rpm and 4℃ for 10 min, after which the samples were packaged individually. Then, the cecal contents were taken in a sterile environment, after long-term cryopreservation at -80℃. Then, the mice's liver and White adipose tissue were weighed to calculate the liver and white adipose indices. Certain liver specimens were subjected to Oil Red O and H\u0026amp;E staining procedures. Remaining livers were stored in a low-temperature refrigerator at -80℃ for the next experiment.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. HE Staining\u003c/h2\u003e\u003cp\u003eLiver tissues were fixed in 4% paraformaldehyde at room temperature for 48 h, embedded in paraffin, and sectioned at 4 \u0026micro;m. The sections were stained with hematoxylin and eosin (H\u0026amp;E staining kit, Solarbio, Beijing, China) and examined under a light microscope (\u0026times;20) for pathological assessment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Oil Red O Staining\u003c/h2\u003e\u003cp\u003eLiver tissues were fixed in 4% paraformaldehyde at room temperature for 48 h and then cryosectioned. The frozen sections were stained with Oil Red O for 10 min, differentiated in 60% isopropanol, and rinsed in distilled water. Subsequently, all sections were counterstained with hematoxylin for 2 min, washed in pure water, differentiated in 60% alcohol for 6 s, and rinsed again. Finally, sections were covered with glycerol gelatin. Histological structure and lipid deposition in mouse liver were observed under a microscope.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Biochemical Analysis\u003c/h2\u003e\u003cp\u003eA biochemical analyzer was employed automatically to measure four blood lipid parameters (TC, TG, LDL-C, HDL-C), along with AST and ALT levels in serum, for the assessment of abnormal blood lipids and liver damage.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.8. 16S rRNA sequencing\u003c/h2\u003e\u003cp\u003eGenomic DNA was isolated from the sample using the CTAB technique, with the purity of the DNA subsequently evaluated via agarose gel electrophoresis. A suitable quantity of sample DNA was transferred into a centrifuge tube (1 ng/\u0026micro;l). We performed PCR amplification by utilizing diluted genomic DNA as the template. We employed New England Biolabs' Phase\u0026reg; PCR Master Mix having high fidelity and GC Buffer, along with an efficient high-fidelity enzyme. Additionally, we utilized V3/V4 primers with Barcode for the amplification process (341F: CCTAYGGGRBGCASCAG; 806R: GGACTACNNGGGTATCTAAT). The detection of PCR products involved electrophoresis, utilizing agarose gel at a concentration of 2%. Subsequently, magnetic bead purification was conducted on PCR products that successfully met the criteria set by the test. For library construction, they utilized the TruSeq\u0026reg; DNA PCR-Free Sample Preparation Kit. The resulting library was then evaluated for quantity using Qubit and qPCR techniques. Once the library passed the quality control, sequencing was carried out using the NovaSeq6000 machine. The Uparse algorithm (obtained from USARCH v7 software, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.drive5.com/Uparse/\u003c/span\u003e\u003cspan address=\"http://www.drive5.com/Uparse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) must be employed. It clusters every Effective Tag from all samples with a 97% level of consistency regarding their identity. Mothur method and SILVA138.1 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arb-silva.de/\u003c/span\u003e\u003cspan address=\"http://www.arb-silva.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to annotate and analyze species (threshold range of 0.8-1). R package, GraphPad Prism 8.0 and other software were utilized to examine the diversity of microbial composition in the samples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.9. Linkage analysis of gut microbiota and metabolites\u003c/h2\u003e\u003cp\u003eThe correlation between differential microorganisms in cecal contents and differential metabolites in serum Ctrl and HFD groups was examined using Spearman correlation analysis. Metabolite and microbial data were imported with significant differences into the Tutools platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cloudtutu.com\u003c/span\u003e\u003cspan address=\"http://www.cloudtutu.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Spearman correlation analysis was conducted, and the correlation coefficient was converted into a thermogram to display the correlation between different metabolites and gut microbiota visually.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.10. LC-MS analysis of the serum\u003c/h2\u003e\u003cp\u003eThe ice blocks in the sample were completely melted after removing it from the \u0026minus;\u0026thinsp;80\u0026deg;C refrigerator and thawing it on ice. Then, 50 \u0026micro;l of the sample was transferred into the numbered centrifuge tube that corresponded to it. Subsequently, 300 \u0026micro;l of a 20% acetonitrile and methanol solution, acting as the internal standard for extraction, was added. This mixture was then subjected to vigorous vortexing for 3 min, succeeded by centrifugation at 12,000 rpm at a temperature of 4\u0026deg;C for 15 min. Subsequently, 200 \u0026micro;l of the supernatant was carefully moved to a distinct tube for centrifugation at -20\u0026deg;C for half an hour. Next, a second centrifugation at 12,000 rpm for 3 min was performed at 4\u0026deg;C. In the final step, 180 \u0026micro;l of the surface liquid was transferred into the inner liner of a vial for subsequent analysis using a spectrometer.\u003c/p\u003e\u003cp\u003eThe UPLC analysis was performed utilizing a Shimadzu LC-30A system (Japan) with a BEH C18 chromatography column (2.1\u0026times;100 mm, 1.8 \u0026micro;m; Waters, MA, USA). The operating parameters included a flow rate of 4 \u0026micro;l/min, a column temperature of 40\u0026deg;C, and a sample injection volume of 2 \u0026micro;l. The mobile phase for the UPLC system consisted of 0.1% formic acid in water (A) and acetonitrile (B). A gradient elution method was employed for the separation process: starting with 5% B at 0 min, increasing from 5% to 90% B over 11 min, maintaining at 90% B from 11 to 12 min, decreasing back to 5% B from 12 to 12.1 min, and then holding at 5% B from 12.1 to 14 min.\u003c/p\u003e\u003cp\u003eThe electric spray ionization (ESI) source-equipped SCIEX TripleTOF 6600\u0026thinsp;+\u0026thinsp;located in Foster City, CA, USA was utilized to acquire the mass spectrum data at ESI\u003csup\u003e+\u003c/sup\u003e and ESI\u003csup\u003e\u0026minus;\u003c/sup\u003e scanning modes. The parameter settings for the operation were: gas temperature 550℃ (ESI\u003csup\u003e+\u003c/sup\u003e) and 450℃ (ESI\u003csup\u003e\u0026minus;\u003c/sup\u003e); Ion Source Gas 50 psi; Capillary voltage 5500 V (ESI\u003csup\u003e+\u003c/sup\u003e) and \u0026minus;\u0026thinsp;4500V (ESI\u003csup\u003e\u0026minus;\u003c/sup\u003e); Declustering Potential 60 V. The mass interval was set from 50 to 1000 Da.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.11. Data processing\u003c/h2\u003e\u003cp\u003eProteo Wizard was utilized for the conversion of raw data into the mzXML format. The XCMS software was then used for peak extraction, followed by adjustments to align the retention times. The \"SVR\" method adjusted the peak area and filtered out peaks with a missing rate greater than 50% and an RSD larger than 30% in each sample group. The metabolites identified and calibrated were obtained by conducting searches in both the laboratory's self-constructed databases and publicly available databases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.12. qPCR\u003c/h2\u003e\u003cp\u003eTotal RNA was extracted from liver tissue utilizing the Total RNA Isolation Kit V2 (Vazyme, China). The qPCR method system: 40 cycles of PCR, each cycle comprising denaturation at 95\u0026deg;C for 10 s and annealing at 60\u0026deg;C for 30 s. A dissolving curve analysis concluded the procedure, progressively thermal treatment from 60 to 95\u0026deg;C at a rate of 0.05\u0026deg;C/s. The 2\u003csup\u003e-ΔΔCt\u003c/sup\u003e method was applied in this research. Please consult Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e for the detailed primer sequences employed in this research.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.13. Statistical analysis\u003c/h2\u003e\u003cp\u003eThe statistical analyses and the creation of histograms were conducted utilizing GraphPad Prism (8.0, San Diego, USA). Results were presented as the average accompanied by the standard deviation. The examination of variance within groups for differences was executed utilizing ANOVA, supplemented by Tukey's post hoc and t-tests (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For PCoA and Rank abundance curve analyses, R software (Version 4.1.2) was used. For LEfSe analysis, the LEfSe software was used with a filtering value of 4 for the default LDA score. Additionally, the adoption of PCA was carried out using R (base package, version 3.5.1), while R (MetaboAnalystR, version 1.0.1) was utilized for OPLS-DA.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.1. UPLC-QQQ-MS/MS examination of the primary bioactive compounds in YDPG\u003c/h2\u003e\u003cp\u003eTo serve as reference standards, Baicalin, geniposide, paeoniflorin, liquiritin, glycyrrhizic acid, and gentiopicroside were utilized as quality control components. The reference standards and typical total ion chromatograms [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] of YDPG were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicated the analysis results of UPLC-QQQ-MS/MS for YDPG. The quality control compounds in YDPG were identified as gentiopicroside in \u003cem\u003eG. scabra, g\u003c/em\u003eeniposide in \u003cem\u003eG. jasminoides\u003c/em\u003e, liquiritin and glycyrrhizic acid in \u003cem\u003eG. uralensis\u003c/em\u003e, baicalin in \u003cem\u003eS. baicalensis\u003c/em\u003e, paeoniflori in \u003cem\u003eP. lactiflora\u003c/em\u003e. The levels of specific compounds in YDPG were analyzed and found to be 10.056 mg/g of gentiopicroside, 13.113 mg/g of geniposide, 0.798 mg/g of liquiritin, 3.009 mg/g of glycyrrhizic acid, 6.576 mg/g of baicalin, and 8.771 mg/g of paeoniflori.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults of UPLC-QQQ-MS/MS analysis of YDPG.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompound\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFormula\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdducts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMRM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePeak Area\u003c/p\u003e\u003cp\u003e(reference standards)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePeak Area\u003c/p\u003e\u003cp\u003e(sample)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eContent (mg/g)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeniposide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003csub\u003e17\u003c/sub\u003eH\u003csub\u003e24\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM-H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e387.10 \u0026gt;\u003c/p\u003e\u003cp\u003e225.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8562.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e56135.746\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.112\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePaeoniflorin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003csub\u003e23\u003c/sub\u003eH\u003csub\u003e28\u003c/sub\u003eO\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM\u0026thinsp;+\u0026thinsp;FA\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e525.45 \u0026gt;\u003c/p\u003e\u003cp\u003e449.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e718055.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3149042.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e8.771\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiquiritin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e22\u003c/sub\u003eO\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM-H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e417.36\u0026thinsp;\u0026gt;\u0026thinsp;255.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2879280.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1149247.375\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBaicalin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e18\u003c/sub\u003eO\u003csub\u003e11\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM-H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e445.03\u0026thinsp;\u0026gt;\u0026thinsp;269.\u003c/p\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2706863.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e8899785.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e6.576\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGlycyrrhizic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003csub\u003e42\u003c/sub\u003eH\u003csub\u003e62\u003c/sub\u003eO\u003csub\u003e16\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM-H\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e821.79 \u0026gt;\u003c/p\u003e\u003cp\u003e351.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e143790.766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e216338.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGentiopicroside\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eO\u003csub\u003e9\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eM\u0026thinsp;+\u0026thinsp;H\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e374.10\u0026thinsp;\u0026gt;\u0026thinsp;195.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11831.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e59492.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e10.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.2. YDPG reduced body weight, liver index, and white fat index in HFD-induced NAFLD\u003c/h2\u003e\u003cp\u003eThe weight of HFD rats increased with an increase in feeding time, as demonstrated in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA. After an eight-week period, the HFD category's weight was substantially greater than that of the Ctrl group, as depicted in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB. Blood samples taken from the Ctrl and model groups were then centrifuged (3,000 rpm, 4℃, 10 min), and the supernatant was analyzed to measure four blood lipid parameters. The HFD category displayed significantly higher levels of lipid parameters compared to the Ctrl category, as depicted in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC-F. These results indicate that a high-fat diet has a notable impact on lipid levels in the body.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Ctrl and HFD groups were given physiological saline, and the drug groups was given different concentrations of drug solutions (HFD\u0026thinsp;+\u0026thinsp;YDPG-L (0.151 g/kg), HFD\u0026thinsp;+\u0026thinsp;YDPG-M (0.302 g/kg), and HFD\u0026thinsp;+\u0026thinsp;YDPG-H (0.604 g/kg) for 28 days to test the therapeutic impact of YDPG. Throughout the treatment timeframe, mice body weight in each group was registered in correlation with their increasing feeding duration. The weight of the Ctrl group mice remained stable, while the model group mice gained weight and exhibited depilation on their backs and tails. However, the weight of the treated group mice first decreased and then remained at a certain level, and there was no depilation on their backs and tails (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). Extended ingestion of a HFD can potentially cause elevated liver and white fat indexes. In this study, the liver index in the HFD category was substantially greater contrasted with the Ctrl category. In contrast, the YDPG administration group could remarkably reduce the liver index, with the best mid dose efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The Ctrl group exhibited a smaller index of white fat in the epididymal region contrasted with the model category. The YDPG administration group diminished mice's liver index without a dose-dependent relationship (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The results indicate that YDPG significantly reduces body weight, liver index, and epididymal white fat index in HFD group.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.3. YDPG decreased liver tissue damage and inflammation in HFD-induced NAFLD\u003c/h2\u003e\u003cp\u003eThe HE staining method can effectively observe liver tissue's structure and pathological changes. In this study, the HFD had disordered liver tissue structure, uneven liver cell arrangement, enlarged liver nuclei, and hepatic steatosis (vacuolar structure). In the YDPG treatment group, the overall disorder of liver tissue structure was alleviated, resulting in a notable reduction in the level of hepatic steatosis (vacuolar structure). The size of the nuclei in the HFD\u0026thinsp;+\u0026thinsp;YDPG-M group was notably smaller (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). AST and ALT are important indicators for evaluating liver tissue damage. The findings of this investigation demonstrate significantly elevated levels of AST and ALT in HFD group contrast to the Ctrl group. After YDPG administration, the AST and ALT levels were significantly reduced. Among them, HFD\u0026thinsp;+\u0026thinsp;YDPG-L and HFD\u0026thinsp;+\u0026thinsp;YDPG-M could significantly reduce the AST, while HFD\u0026thinsp;+\u0026thinsp;YDPG-M could significantly reduce the ALT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Therefore, the HFD\u0026thinsp;+\u0026thinsp;YDPG-M had a better therapeutic effect on liver damage in HFD prompted NAFLD.\u003c/p\u003e\u003cp\u003eTo assess the inflammatory reaction due to liver damage further, qPCR was utilized to quantify the quantities of TNF-α, IL-6, and IL-1β in liver tissue. The results showed a notable elevation in the mRNA levels of TNF-α, IL-1β, and IL-6 in the HFD group relative to the Ctrl group. Nevertheless, the expression degrees of these genes were markedly decreased in the YDPG-treated groups. Specifically, the HFD\u0026thinsp;+\u0026thinsp;YDPG-M treatment remarkably reduced the IL-1β mRNA level, while HFD\u0026thinsp;+\u0026thinsp;YDPG-L effectively reduced TNF-α and IL-6 mRNA levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.4. YDPG lipid accumulation and four items of blood lipid in HFD-induced NAFLD\u003c/h2\u003e\u003cp\u003eThe Oil Red O staining results clearly showed minimal lipid droplet aggregation in the control group, whereas a significant accumulation of lipid droplets was observed in the high-fat diet group. This indicates that the high-fat diet caused an increase in lipid droplet formation, highlighting the impact of diet on lipid metabolism. The YDPG groups exhibited a notable reduction in liver fat droplets and an improvement in steatosis. Notably, the administration of HFD\u0026thinsp;+\u0026thinsp;YDPG-L substantially diminished the accumulation of liver fat droplets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The degrees of the four blood lipid parameters were substantially rose in the HFD category in comparison to the Ctrl group. Alternatively, the YDPG treatment group significantly inhibit four blood lipid items levels. The administration of HFD\u0026thinsp;+\u0026thinsp;YDPG-M proved to be more effective in reducing the four blood lipid levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). For LDL-C indicators, there was a dose-dependent tolerance of YDPG drugs. The findings suggest that YDPG could potentially offer protective benefits against steatosis in mouse livers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.5. YDPG regulated configuration of gut microbiota in HFD-prompted NAFLD\u003c/h2\u003e\u003cp\u003eSince gut microbiota composition is intimately connected to NAFLD, we used 16S rRNA sequencing to compare gut microbiota proportions in HFD groups and YDPG groups. The Rank Abundance curve, which illustrates both species abundance and evenness, was utilized for data interpretation. The curve range of Ctrl, HFD, HFD\u0026thinsp;+\u0026thinsp;YDPG-L, and HFD\u0026thinsp;+\u0026thinsp;YDPG-M was larger; the species wind was higher. The curve was relatively flat, and the species distribution was uniform compared to HFD\u0026thinsp;+\u0026thinsp;YDPG-H (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This study analyzed 8864 fecal microbiota sequencing readings, among which the average readings for the Ctrl, HFD, HFD\u0026thinsp;+\u0026thinsp;YDPG-L, HFD\u0026thinsp;+\u0026thinsp;YDPG-M, and HFD\u0026thinsp;+\u0026thinsp;YDPG-H groups were 1884, 1834, 1787, 1905, and 1454, respectively. Further, the OUT results of each group were displayed through an upset, with points and lines representing whether there were common OUTs between the groups. Among them, there are 673 outputs shared by Ctrl, HFD, HFD\u0026thinsp;+\u0026thinsp;YDPG-L, HFD\u0026thinsp;+\u0026thinsp;YDPG-M, and HFD\u0026thinsp;+\u0026thinsp;YDPG-H, while there are 64 outputs shared by Ctrl and YDPG groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eBased on the PCoA diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), a clear distinction was observed between the Ctrl group compared to both the YDPG groups and the HFD group in relation to the phylum level. \u003cem\u003eFirmicutes\u003c/em\u003e showed a notable rise while \u003cem\u003eFusobacteria\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e exhibited a decrease within the HFD group. The YDPG treatment, however, managed to restore the levels of \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and \u003cem\u003eBacteroidetes\u003c/em\u003e to a certain degree. The \u003cem\u003eFirmicutes\u003c/em\u003e to \u003cem\u003eBacteroidetes\u003c/em\u003e ratio, associated with metabolic diseases, was substantially rose in the HFD category. On the contrary, the HFD\u0026thinsp;+\u0026thinsp;YDPG-L and HFD\u0026thinsp;+\u0026thinsp;YDPG-M groups showed decreased ratios compared to the HFD group, with the HFD\u0026thinsp;+\u0026thinsp;YDPG-M group displaying a significantly lower ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eF depicts the bacterial community's genus-level abundance. The HFD group experienced substantial increases in \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIleibacterium\u003c/em\u003e, \u003cem\u003eAllobaculum\u003c/em\u003e, and \u003cem\u003eEnterococcus\u003c/em\u003e (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the HFD category, \u003cem\u003eDubosiella\u003c/em\u003e was identified to be less abundant. However, the categories treated with HFD\u0026thinsp;+\u0026thinsp;YDPG-L and HFD\u0026thinsp;+\u0026thinsp;YDPG-M recorded significant declines in the abundances of \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIleibacterium\u003c/em\u003e, and \u003cem\u003eEnterococcus\u003c/em\u003e. Furthermore, \u003cem\u003eAllobaculum\u003c/em\u003e's presence notably diminished in both the HFD\u0026thinsp;+\u0026thinsp;YDPG-L and HFD\u0026thinsp;+\u0026thinsp;YDPG-H groups. On the other hand, \u003cem\u003eDubosella\u003c/em\u003e saw a considerable increase in abundance in the HFD\u0026thinsp;+\u0026thinsp;YDPG-M and HFD\u0026thinsp;+\u0026thinsp;YDPG-H groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). Further analysis using LEfSe indicated a high pervasiveness of \u003cem\u003eClostridioides\u003c/em\u003e at the genus level in the HFD category. Conversely, at the genus level, the pervasiveness of \u003cem\u003eAerococcus\u003c/em\u003e, \u003cem\u003eColidextribacter\u003c/em\u003e, and \u003cem\u003eLysinibacillus\u003c/em\u003e was increased by YDPG-L treatment, whereas YDPG-H treatment raised the levels of \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eIleibacterium\u003c/em\u003e, and \u003cem\u003eStaphylococcus\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e3.6. YDPG affected the distribution of serum metabolites in HFD-induced NAFLD\u003c/h2\u003e\u003cp\u003eThe composition of gut microbiota also impacts metabolic products, thereby influencing the occurrence and progression of various diseases. In this investigation, alterations in serum metabolites were explored using non-targeted metabolomics. First, perform PCA and OPLS-DA analysis on Ctrl, HFD, HFD\u0026thinsp;+\u0026thinsp;YDPG-L, HFD\u0026thinsp;+\u0026thinsp;YDPG-M, and HFD\u0026thinsp;+\u0026thinsp;YDPG-H samples. The PCA results showed that Ctrl, HFD, and HFD\u0026thinsp;+\u0026thinsp;YDPG were significantly separated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). The OPLS-DA results indicated that Ctrl, HFD, HFD\u0026thinsp;+\u0026thinsp;YDPG-M, and HFD\u0026thinsp;+\u0026thinsp;YDPG-H can be distinguished. However, HFD\u0026thinsp;+\u0026thinsp;YDPG-M, HFD\u0026thinsp;+\u0026thinsp;YDPG-H, and HFD\u0026thinsp;+\u0026thinsp;YDPG-L cannot be remarkably distinguished (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Further, R\u0026sup2;X, R\u0026sup2;Y and Q\u0026sup2; were evaluated the reliability of the OPLS-DA model. The Q2\u0026thinsp;=\u0026thinsp;0.801, R\u0026sup2;Y\u0026thinsp;=\u0026thinsp;0.94, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, which implied that the system had the best predictive ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). A screening method was utilized to pinpoint differential metabolites distinguishing the Ctrl from the HFD groups (FC\u0026thinsp;\u0026ge;\u0026thinsp;2 or \u0026le;\u0026thinsp;0.5, VIP\u0026thinsp;\u0026ge;\u0026thinsp;1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This analysis uncovered that 125 differential metabolites experienced an increase, while 228 were decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Specifically, there were 109, 166, and 132 differential metabolites identified when comparing the HFD group to the HFD\u0026thinsp;+\u0026thinsp;YDPG-L, HFD\u0026thinsp;+\u0026thinsp;YDPG-M, and HFD\u0026thinsp;+\u0026thinsp;YDPG-H groups, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.7. YDPG alleviated HFD-induced NAFLD by regulating short-chain fatty acids, bile acids, and neurotransmitters\u003c/h2\u003e\u003cp\u003eBy analyzing the cecum contents, we have determined that YDPG can regulate the abundance of \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIleibacterium\u003c/em\u003e, \u003cem\u003eAllobaculum\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, and \u003cem\u003eDubosella\u003c/em\u003e. Research has found that \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIleibacterium\u003c/em\u003e, \u003cem\u003eAllobaculum\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, and \u003cem\u003eDubosella\u003c/em\u003e can affect the production of bile acids, neurotransmitters, and short-chain fatty acids. The interaction among these compounds plays a crucial role in the onset and advancement of NAFLD. Therefore, we conducted an in-depth analysis of the differential metabolites, like short-chain fatty acids, bile acids, and neurotransmitters. Within the HFD group, a number of significant disparities were noted. In particular, variations in 19 short-chain fatty acids, 22 bile acids, and two neurotransmitters were significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003eThe pathways enriched in these differential metabolites include linoleic acid metabolism, ovarian steroidogenesis, cortisol synthesis and secretion, primary bile acid biosynthesis, steroid hormone biosynthesis, bile secret, cAMP signaling pathway, and PPAR signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). Moreover, the significantly different TOP signaling pathways are tightly linked to lipid metabolism, indicating that YDPG regulates lipid metabolism in treating NAFLD.\u003c/p\u003e\u003cp\u003eThe metabolites highlighted within the red box in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003eF show significant variations across the Ctrl, HFD, and YDPG groups. Among these, indole-3-propionic acid, 3-(methylthio)propionic acid, and valeric acid are distinct short-chain fatty acids. Notably, the HFD group exhibited a significant rise in 3-(methylthio)propionic acid and valeric acid levels, while these levels were substantially diminished in the HFD\u0026thinsp;+\u0026thinsp;YDPG-L group. Indole-3-propionic acid exhibited a substantial diminishment in the HFD category, but its content remarkably increased in the HFD\u0026thinsp;+\u0026thinsp;YDPG-H group, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eA. Nordeoxycholic acid, glycolic acid, tauoursodeoxycholic acid, and Taurochenodeoxycholic acid were identified as notable bile acids with significant differences. The HFD group demonstrated a significant elevation in glycolic acid, taurosodeoxycholic acid, taurocholic acid, and taurochenodeoxycholic acid, while their content decreased significantly in the HFD\u0026thinsp;+\u0026thinsp;YDPG-L group. Conversely, nordeoxycholic acid experienced a significant decrease in the HFD group, but its content increased remarkably in the HFD\u0026thinsp;+\u0026thinsp;YDPG-H group, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eB. Serotonin, a neurotransmitter with differential expression, showed a remarkable reduction in the HFD group, while its content markedly increased in the HFD\u0026thinsp;+\u0026thinsp;YDPG-M group, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Results of the correlation analysis between gut microbiota and metabolites\u003c/h2\u003e\u003cp\u003eThe correlation between differential microorganisms in cecal contents and differential metabolites in serum Ctrl and HFD groups was examined using Spearman correlation analysis. Glycolic acid, taurosodeoxycholic acid, taurocholic acid, and taurochenodeoxycholic acid were remarkably favorably linked with \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIlleiberium\u003c/em\u003e, and \u003cem\u003eEnterococcus\u003c/em\u003e. The taurochenodeoxycholic acid, valeric acid, and 3- (Methylthio) propionic acid components significantly correlated with \u003cem\u003eAllobaculum\u003c/em\u003e. A notable negative linkage exists between serotonin and nordeoxycholic acid components with \u003cem\u003eAllobaculum\u003c/em\u003e. Additionally, serotonin demonstrates a significant negative correlation with \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIleiberium\u003c/em\u003e, and \u003cem\u003eEnterococcus\u003c/em\u003e. Lastly, the indole-3-propionic acid component exhibits a significant negative correlation with \u003cem\u003eIleibacterium\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e. A significant relationship exists between differential endophytic bacteria and the metabolites modulated by YDPG, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.9. YDPG-regulated lipid metabolism in HFD-induced NAFLD\u003c/h2\u003e\u003cp\u003eThe pathways enriched in these differential metabolites include linoleic acid metabolism, ovarian steroidogenesis, cortisol synthesis and secretion, primary bille acid biosynthesis, steroid hormone biosynthesis, cAMP signaling pathway, and PPAR signaling pathway. Therefore, we further validated the gene mRNA expression level of YDPG-regulated lipid metabolism-related signaling pathways through qPCR and further elucidated the mechanism of YDPG efficacy. Within the primary bile acid biosynthesis pathway, the expression degrees of Cyp39a1, and Hsd3b7 were noticeably elevated in HFD group. However, the administration of YDPG effectively decreased the expression levels of these enzymes (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). HFD boosted Pparg mRNA expression in the PPAR signaling pathway. Still, the YDPG treatment group severely decreased it. In HFD group, the level of Ppard mRNA expression showed a notable decrease, which was observed to significantly increase upon YDPG administration (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). Moreover, the HFD group also exhibited remarkable enhancements in the mRNA expression degrees of Cyp7a1, LXRa (Nr1h3), and Hmgcr in the cholesterol metabolism pathway. Conversely, the YDPG-treated group showed decreased gene expression compared to the HFD group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Cyp7a1 can regulate cholesterol metabolism and primary bile acid biosynthesis. In the HFD groups, Abcg5, Abcg8, Nfkb1, Gadl1, Fmo1 and Scd1 expression levels were observed to be markedly elevated, while the YDPG treatment group significantly inhibited gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eD-\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eG). Cyp8b1, Cyp27a1, Srebp2 and Acsl1 expression degrees were insignificant in the Ctrl, HFD, and YDPG categories (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). To summarize, YDPG has the potential to enhance HFD-induced NAFLD by modulating the signaling pathway of PPAR, metabolism of cholesterol, secretion of bile, taurine and hydrogen metabolism, and other pathways associated with lipid metabolism.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eFatty liver includes alcoholic fatty liver and NAFLD. Excessive and prolonged intake of alcohol leads to the progression of alcoholic fatty liver. Moreover, this condition possesses the potential to evolve into alcoholic hepatitis, liver fibrosis, and cirrhosis. When there is no alcohol consumption, fat accumulates abnormally in the liver to form NAFLD. It is related to metabolic syndrome, obesity, hypertension, hyperlipidemia, diabetes and other metabolic diseases. Previous studies have shown that YDPG improves acute alcoholic hepatitis by inhibiting inflammatory responses and oxidative stress levels [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the effect of YDPG on NAFLD has not been reported. This research results indicated that YDPG exerts protective effects against HFD-induced NAFLD in mice, ameliorating key pathological features including body weight gain, hepatic steatosis, liver injury, inflammation, and dyslipidemia. More importantly, our integrated approach combining pharmacochemical analysis, gut microbiota profiling, and serum metabolomics reveals that the therapeutic mechanism of YDPG is multi-targeting and holistic, primarily mediated through the remodeling of gut microbiota structure and the subsequent regulation of microbial-host co-metabolites, including short-chain fatty acids, bile acids, and neurotransmitters, which ultimately modulates hepatic lipid metabolic pathways [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe most salient therapeutic advantage of YDPG, particularly when contrasted with current single-target pharmacotherapies like the THR-β agonist Rezdiffra (resmetirom), lies in its multi-component, multi-pathway holistic regulatory strategy. Our UPLC-QQQ-MS/MS analysis confirmed that YDPG contains a spectrum of bioactive compounds, such as baicalin, geniposide, glycyrrhizic acid, and paeoniflorin, which are known to possess anti-inflammatory, antioxidant, and lipid-modulating properties [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This phytochemical complexity allows YDPG to synchronously target the core axes of NAFLD pathogenesis\u0026mdash;gut dysbiosis, metabolic disturbance, and inflammation\u0026mdash;a feat difficult to achieve with a single synthetic molecule. While Rezdiffra offers a targeted, potent agonism of THR-β, its application is restricted to non-cirrhotic patients and is associated with a notable incidence of gastrointestinal adverse effects [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In contrast, YDPG exhibited remarkable tolerability in our model, aligning with its historical use in various liver conditions, and it produced therapeutic effects across a wide range of metabolic and inflammatory parameters without observed adverse events. However, since Rezdiffra is currently unavailable through official channels in China, we did not conduct a direct experimental comparison in our study.\u003c/p\u003e\u003cp\u003eA key mechanistic insight from this study is the pivotal role of gut-liver axis modulation in YDPG's efficacy. The HFD-induced dysbiosis, characterized by an elevated \u003cem\u003eFirmicutes/Bacteroidetes\u003c/em\u003e ratio and a bloom of pro-inflammatory genera like \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIleibacterium\u003c/em\u003e, and \u003cem\u003eEnterococcus\u003c/em\u003e, was significantly reversed by YDPG treatment. Concomitantly, YDPG promoted the abundance of beneficial genera like \u003cem\u003eDubosiella\u003c/em\u003e and \u003cem\u003eAkkermansia\u003c/em\u003e, which are associated with improved gut barrier integrity and metabolic health [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This microbiota remodeling directly translated into a normalization of critical microbial-derived metabolites. YDPG administration significantly rectified the imbalances in serum levels of bile acids (e.g., taurocholic acid, taurochenodeoxycholic acid), short-chain fatty acids (e.g., valeric acid, indole-3-propionic acid), and the neurotransmitter serotonin. The strong correlations between these differential metabolites and the altered gut microbes, as revealed by Spearman analysis, solidify the premise that YDPG acts first on the gut ecosystem, with systemic metabolic consequences.\u003c/p\u003e\u003cp\u003eShort-chain fatty acid (SCFA), including acetic acid (AA), propionic acid (PA), and butyric acid (BA) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], is a key bridge connecting intestinal microbiota and the body, which can enrich beneficial bacteria, inhibit harmful bacteria, and affect intestinal health and systemic metabolism. PA can attenuate steatohepatitis by inhibiting endotoxin leakage [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. BA alleviated lipid formation and inflammation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In untargeted metabolomics data analysis, two propionic acids differed significantly between YDPG and HFD. The content of Indole-3-propionic acid displayed a significant reduction within the HFD group. Conversely, the YDPG group exhibited a notable ability to increase its concentration, enhance the abundance of advantageous microorganisms, and suppress the proliferation of detrimental bacteria. Furthermore, it was found that the HFD group remarkably increased valeric acid content. However, after YDPG administration, the valeric acid content was significantly inhibited. Among them, 3- (Methylthio) propionic acid and valeric acid content were positively linked to the concentration of \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIleibacterium\u003c/em\u003e, \u003cem\u003eAllobaculum\u003c/em\u003e, and \u003cem\u003eEnterococcus\u003c/em\u003e. Nonetheless, there's a significant negative linkage between indole-3-propionic acid and the abundance of \u003cem\u003eIleibacterium and Enterococcus\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eBile acids are an important bile component produced by cholesterol metabolism. Bile acids play diverse roles in the human body, encompassing synthesis and absorption of cholesterol [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], antibacterial effects [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], liver metabolism [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and impact on the occurrence and progression of NAFLD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Five different bile acids were were pinpointed in this study between the YDPG and HFD categories. Among them, the concentrations of Taurocholic acid, Taurosodeoxycholic acid, Glycolic acid, and Taurochenodeoxycholic acid remarkably elevated in the HFD category and significantly diminished after administration of YDPG. Subsequently, the differences between the HFD and TDPG groups of Nordeoxycholic acid were opposite to the results of the other four bile acids. Taurocholic acid, Taurosodeoxycholic acid, Glycocholic acid, Taurochenodeoxycholic acid, and Nordeoxycholic acid were remarkably favorably linked to the concentration of \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIlleiberium\u003c/em\u003e, \u003cem\u003eAllobaculum\u003c/em\u003e, and \u003cem\u003eEnterocus\u003c/em\u003e. The decline in the content of bile acids was in line with the diminishment in the concentration and variety of gut microbiota. Results suggested that YDPG elicited favorable outcomes for the host through the regulation of the structure and metabolites of intestinal microorganisms.\u003c/p\u003e\u003cp\u003eFurthermore, the downstream hepatic effects of this gut-centric action were elucidated through gene expression analysis. YDPG effectively downregulated the expression of key genes driving lipogenesis (Srebp2, Scd1), cholesterol synthesis (Hmgcr), and bile acid synthesis (Cyp7a1, Cyp39a1), while modulating pivotal nuclear receptors (Pparg, Ppard, LXRa) involved in lipid homeostasis. This coordinated suppression of anabolic pathways, coupled with a potential enhancement of fatty acid oxidation (suggested by elevated Ppard), underpins the observed alleviation of hepatic steatosis and lipotoxicity [\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The fact that these metabolic benefits were achieved without a strict dose-dependent relationship suggests a homeostatic-restoring effect characteristic of multi-target botanical drugs, wherein a moderate dose (HFD\u0026thinsp;+\u0026thinsp;YDPG-M) often achieved optimal efficacy by balancing synergistic actions without triggering compensatory mechanisms.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, our findings provide robust preclinical evidence that YDPG is a promising multi-target therapeutic agent for NAFLD. Its unique strength stems from a systems-level approach that concurrently ameliorates gut microbiota dysbiosis, corrects deranged microbial metabolism, and dampens hepatic inflammatory and metabolic stress, thereby addressing the intricate pathophysiology of NAFLD more comprehensively than current single-target options. This study not only validates the traditional use of YDPG but also propels it into the modern therapeutic landscape as a gut-microbiota-focused regulator of metabolic health. Future clinical trials are warranted to translate these promising findings into a validated treatment strategy for NAFLD patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJH:\u003c/strong\u003e Validation, Investigation, Visualization, Writing—original draft, Writing—review and editing, Data curation.\u0026nbsp;\u003cstrong\u003eSL:\u003c/strong\u003e Validation, Investigation, Visualization, Writing—review and editing, Validation.\u0026nbsp;\u003cstrong\u003eFZ:\u003c/strong\u003e Investigation, Methodology, Visualization.\u0026nbsp;\u003cstrong\u003eHX:\u003c/strong\u003e Investigation, Methodology.\u0026nbsp;\u003cstrong\u003eXL:\u0026nbsp;\u003c/strong\u003eConceptualization, Project administration, Supervision, Writing–review and editing.\u0026nbsp;\u003cstrong\u003eHY:\u0026nbsp;\u003c/strong\u003eConceptualization, Project administration, Supervision, Writing—review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Scientific and technological innovation project of China Academy of Chinese Medical Sciences (CI2021B017), Scientific and Technological Innovation Project of CACMS (CI2021A05032), National Natural Science Foundation of China (82174238), and the Fundamental Research Funds for the Central Public Welfare Research Institutes (RXRC2022002, ZZ13-YQ-080, and XTCX2021001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (PRJCA045435) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. Shared URL: https://ngdc.cncb.ac.cn/gsa/s/T24aF4Jk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGao L-L, Ma J-M, Fan Y-N, Zhang Y-N, Ge R, Tao X-J, et al. Lycium barbarum polysaccharide combined with aerobic exercise ameliorated nonalcoholic fatty liver disease through restoring gut microbiota, intestinal barrier and inhibiting hepatic inflammation. Int J Biol Macromol. 2021;183:1379\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePowell EE, Wong VW-S, Rinella M. Non-alcoholic fatty liver disease. 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J Hepatol. 2021;75:400\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"chinese-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmed","sideBox":"Learn more about [Chinese Medicine](http://cmjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cmed/default.aspx","title":"Chinese Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Yindan Pinggan capsule, Nonalcoholic fatty liver disease, Lipid metabolism, Gut microbiota, Untargeted metabolomics","lastPublishedDoi":"10.21203/rs.3.rs-7631502/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7631502/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNonalcoholic fatty liver disease (NAFLD) is a common chronic liver disorder with limited treatment options. Yindan Pinggan capsule (YDPG), a traditional Chinese medicine, has demonstrated potential in managing liver diseases, yet its efficacy and mechanisms in NAFLD remain unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA high-fat diet (HFD)-induced NAFLD mouse model was established. The major bioactive components of YDPG, including baicalin, geniposide, and glycyrrhizic acid, were quantified using UPLC-QQQ-MS/MS. Integrated 16S rRNA sequencing and serum metabolomics were employed to analyze gut microbiota and metabolic profiles. qPCR was used to assess gene expression related to lipid metabolism.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eYDPG treatment significantly reduced body weight, liver index, hepatic lipid accumulation, inflammation, and improved serum lipid profiles and liver function (AST/ALT). It reshaped gut microbiota by decreasing harmful genera (e.g., \u003cem\u003eClostridioides\u003c/em\u003e, \u003cem\u003eIleibacterium\u003c/em\u003e) and enriching beneficial ones (e.g., \u003cem\u003eDubosiella\u003c/em\u003e), while regulating key metabolites involving bile acids, short-chain fatty acids, and neurotransmitters. qPCR confirmed modulation of lipid metabolism genes (e.g., Pparg, Cyp7a1, Hmgcr).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eYDPG alleviates NAFLD by modulating the gut-liver axis, restoring gut microbial balance, and correcting metabolic disorders, demonstrating its potential as a multi-target therapeutic agent for NAFLD.\u003c/p\u003e","manuscriptTitle":"Multi-omics analysis reveals gut microbiota remodeling and lipid metabolism regulation during the treatment of nonalcoholic fatty liver disease with Yindan Pinggan capsule","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 14:15:55","doi":"10.21203/rs.3.rs-7631502/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-23T13:14:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T02:41:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14858650914204386103816886829749585592","date":"2025-09-30T01:32:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T01:25:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-25T12:38:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-25T06:53:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Chinese Medicine","date":"2025-09-16T14:17:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"chinese-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cmed","sideBox":"Learn more about [Chinese Medicine](http://cmjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/cmed/default.aspx","title":"Chinese Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0cb3f41e-7dfb-4bde-9eda-20ed580d25c4","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:24:31+00:00","versionOfRecord":{"articleIdentity":"rs-7631502","link":"https://doi.org/10.1186/s13020-026-01351-x","journal":{"identity":"chinese-medicine","isVorOnly":false,"title":"Chinese Medicine"},"publishedOn":"2026-03-24 16:12:28","publishedOnDateReadable":"March 24th, 2026"},"versionCreatedAt":"2025-10-15 14:15:55","video":"","vorDoi":"10.1186/s13020-026-01351-x","vorDoiUrl":"https://doi.org/10.1186/s13020-026-01351-x","workflowStages":[]},"version":"v1","identity":"rs-7631502","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7631502","identity":"rs-7631502","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}