Yinchen Wuling San ameliorates high-fat diet-induced metabolic-associated fatty liver disease in rats by remodeling the gut microbiota

Abstract

BACKGROUND

Metabolic-associated fatty liver disease (MAFLD) is a common metabolic disorder characterized by hepatic steatosis, obesity, abnormal liver function, and dyslipidemia, with limited effective drug options available. Increasing evidence links MAFLD to dysbiosis of the gut microbiota and microbial metabolites, including short-chain fatty acids (SCFAs). We hypothesized that Yinchen Wuling San (YCWLS) mitigates MAFLD by remodeling gut microbiota and associated SCFAs profiles.

AIM

To determine whether YCWLS alleviates MAFLD by inducing gut microbiota remodeling in rats.

METHODS

A high-fat diet was used to induce MAFLD in rats. The animals were assigned to control, model, positive drug (Bifidobacterium quadruple live bacteria tablets, 2.1 g/kg), and YCWLS (6.38 g/kg) groups and treated for 4 weeks. Liver and adipose histopathology was evaluated using hematoxylin-eosin staining. Serum lipids and liver enzymes were measured, gut microbiota was profiled by 16S rRNA sequencing, and fecal SCFAs were quantified using gas chromatography-mass spectrometry.

RESULTS

Compared to the model group, YCWLS reduced the liver index and improved liver injury markers, specifically alanine aminotransferase and aspartate aminotransferase (alanine aminotransferase model 50.05 ± 8.98 vs YCWLS 22.97 ± 2.40 P < 0.01; aspartate aminotransferase model 217.32 ± 13.70 vs YCWLS 122.65 ± 6.49 P < 0.01), alleviating hepatic steatosis and tissue injury. YCWLS also decreased serum triglycerides, total cholesterol, and low-density lipoprotein cholesterol while increasing high-density lipoprotein cholesterol (triglycerides model 1.26 ± 0.18 vs YCWLS 0.61 ± 0.09 P < 0.01; total cholesterol model 2.83 ± 0.30 vs YCWLS 1.79 ± 0.09 P < 0.01; low-density lipoprotein cholesterol model 0.49 ± 0.16 vs YCWLS 0.17 ± 0.02 P < 0.01; high-density lipoprotein cholesterol model 0.50 ± 0.11 vs YCWLS 1.01 ± 0.09 P < 0.01). Microbiota analysis revealed a reduction in Firmicutes-related taxa (including Ruminococcaceae and Clostridiales) and an increase in Verrucomicrobia, Lachnospiraceae, Peptostreptococcaceae, and Akkermansia. Most fecal SCFAs exhibited modest, non-significant increases; however, butyrate displayed a near-significant upward trend (P = 0.052).

CONCLUSION

Yinchen Wuling San improves liver injury and dyslipidemia in MAFLD rats and is associated with the enrichment of Akkermansia and Lachnospiraceae, supporting a gut microbiota-linked mechanism.

Key Words: Metabolic-associated fatty liver disease; Yinchen Wuling San; Gut microbiota; Short-chain fatty acids; Gut-liver axis

Core Tip: Yinchen Wuling San (YCWLS), a classical traditional Chinese medicine formula, alleviated the phenotypes of metabolic-associated fatty liver disease in rats fed a high-fat diet. This improvement was evidenced by enhanced liver histopathology, reduced levels of alanine aminotransferase and aspartate aminotransferase, and corrected dyslipidemia. Profiling of the 16S rRNA revealed that YCWLS reshaped the gut microbiota by significantly enriching Akkermansia and Lachnospiraceae while decreasing Firmicutes-associated taxa. Although there were only modest and non-significant increases in fecal short-chain fatty acids, the observed shift in microbiota was associated with metabolic improvements, suggesting a microbiota-linked mechanism through which YCWLS exerts its effects on metabolic-associated fatty liver disease.

INTRODUCTION

Metabolic-associated fatty liver disease (MAFLD) is characterized by an accumulation of fat in liver cells, where the fat content exceeds 5%. This occurs in the absence of excessive alcohol consumption and other known factors that can cause liver damage[1]. The histological features of MAFLD include simple steatosis, metabolic-associated steatohepatitis, liver fibrosis, and cirrhosis. As obesity and other contributing factors continue to rise, the global prevalence of MAFLD has increased to 30%, making it the most common chronic liver disease[2]. The clinical management of MAFLD primarily involves dietary adjustments, exercise, lifestyle modifications, and pharmacological interventions, including glucagon-like peptide-1 (GLP-1) receptor agonists, peroxisome proliferator-activated receptors agonists, and farnesoid X receptor agonists. However, long-term use of these treatments can result in reduced efficacy and potential adverse effects, such as hepatic and renal impairment. Consequently, it is crucial to identify Chinese herbal formulas that offer superior efficacy and minimal side effects for treating MAFLD. Traditional Chinese medicine (TCM) categorizes MAFLD under syndromes such as “liver stagnation”, “mass accumulation”, and “fatty qi”. Its pathogenesis often involves liver wood excess or deficiency, disrupting the regulatory relationship between liver wood and spleen earth (“liver wood regulates earth, spleen earth nourishes wood”). Impaired liver qi regulation and spleen qi transport lead to the accumulation of internal phlegm and blood stasis, which obstruct the liver collaterals. As a result, TCM often utilizes liver-regulating and spleen-strengthening herbs to treat MAFLD. In this study, we selected YinChen WuLing San (YCWLS), a classic formula from the Golden Cabinet Essential Prescriptions. YCWLS is recognized for its ability to clear heat, drain dampness, strengthen the spleen, and promote diuresis. It has been clinically applied to treat MAFLD[3,4], demonstrating efficacy in repairing liver damage, improving lipid profiles, and reducing liver fibrosis in patients.

Research has revealed that the gut and its symbiotic microbiota can exert bidirectional regulation on the liver via the “gut-liver axis”[5]. The fundamental mechanism entails the ongoing translocation of gut microbiota and their metabolites to the liver via the portal vein, while substances secreted by the liver - such as bile and enzymes - can similarly exert retrograde effects on the gastrointestinal tract through the same portal vein. As a critical regulatory element, the gut microbiota significantly influences host immune homeostasis, metabolic equilibrium, and inflammatory responses through this bidirectional axis[6,7]. Studies have shown[8] that at the population level, the microbiome structure of healthy individuals exhibits high temporal stability, whereas MAFLD patients demonstrate persistent microbiome disturbance. Our research group previously demonstrated significant therapeutic efficacy of YCWLS in treating MAFLD[9]. Although studies confirm that YCWLS improves liver damage and lipid levels in MAFLD patients, the underlying mechanisms remain unclear - particularly regarding whether it exerts its effects by regulating intestinal-liver axis function through modulation of gut microbiota composition and influencing short-chain fatty acids (SCFAs) production. As shown in Figure 1, this study employed a high-fat diet (HFD)-induced MAFLD rat model to investigate the mechanisms by which YCWLS mediates the treatment of MAFLD through gut-to-liver regulation, with the aim of providing insights for the clinical application of YCWLS in MAFLD treatment.

Figure 1 Flow chart of the experiment. LC-MS/MS: Liquid chromatography-tandem mass spectrometry; YCWLS: Yinchen Wuling San; CBLT: Bifidobacterium quadruple live bacteria tablets; SCFA: Short-chain fatty acid.

MATERIALS AND METHODS

Animals

Thirty-six specific pathogen free (SPF)-grade male Sprague-Dawley rats (8 weeks old, 160-200 g) were obtained from Hunan Silaike Jingda Laboratory Animal Co., Ltd. (license No. SYXK(Xiang)2021-0002). Animals were maintained in the SPF animal facility of Hunan University of Chinese Medicine under controlled conditions (12 hours light/dark cycle; 23 °C ± 3 °C; relative humidity 50% ± 5%; 3 rats per cage). All experimental procedures were performed in line with the institutional guidelines for laboratory animal care and were approved by the Animal Ethics Committee of Hunan University of Chinese Medicine (approval No. HNUCM21-2412-01).

Drugs and reagents

Yinchen (Latin name: Artemisiae Scopariae Herba) 30 g, Zexie (Alisma Orientale Juz) 15 g, Zhuling (Polyporus Umbellatus Fr) 10 g, Fuling (Poria Cocos Wolf) 10 g, Atractylodes Macrocephala Koidz 10 g, Cinnamomi Ramulus 6 g were purchased from the First Affiliated Hospital of Hunan University of Chinese Medicine. The formulation dosage was prepared following the clinical experience of Professor Zhao GR, a renowned TCM practitioner in Hunan Province[9], and confirmed by faculty members from the School of Pharmacy at Hunan University of Chinese Medicine. Bifidobacterium quadruple live bacteria tablets (CBLT) (Hangzhou Yuanda Biopharmaceutical Co., Ltd., National Drug Approval No. S20060010, China). Total cholesterol (TC) (batch No. 0567263), triglycerides (TG) (0567107), low-density lipoprotein cholesterol (LDL-C) (0575657), high-density lipoprotein cholesterol (HDL-C) (0575894), alanine aminotransferase (ALT) (24-1231), aspartate aminotransferase (AST) (24-1119) were all tested by the First Affiliated Hospital of Hunan University of Chinese Medicine.

Preparation of YCWLS decoction

The Yinchen, Alisma, Polyporus, Poria, Atractylodes macrocephala, and Cinnamon twigs were mixed following a specified ratio, and then soaked in water for 30 minutes. It was subjected to decoction on high fire until boiling, and then on slow fire for 30 minutes. Next, the medicinal liquid was poured out, water was added and then decocted again as above. The medicinal solution was mixed well and filtered, and the medicine was evaporated and concentrated to prepare a crude medicine with a concentration of 1.09 g/mL, which was kept in a refrigerator at 4 °C until use.

Detection of blood constituents of YCWLS

Preparation of TCM extracts and treatment of biological samples: YCWLS samples were prepared and labeled as Group Y. Briefly, they were thawed, centrifuged at 4 °C and 12000 r/minute for 15 minutes. An aliquot (300 μL) of the supernatant was mixed with 1000 μL of extraction solvent (methanol:acetonitrile:water = 2:2:1, v/v/v). The mixture was vortexed for 30 seconds, sonicated in an ice-water bath for 5 minutes, and then kept at -20 °C for 1 hour. They were then centrifuged (12000 r/minute, 15 minutes), and the upper supernatant was collected and passed through a 0.22 μm microporous membrane filter for further instrumental analysis.

Serum preparation and biological sample processing: After 3 days of acclimatization, 6 SPF-grade SD male rats were randomly assigned to either the drug-containing serum group (K) or the blank serum group (YWD) using a random number table, with 3 rats in each group. Group K received YCWLS at a concentration of 6.38 g/kg via gastric lavage, while group YWD received distilled water via gastric lavage, administered once daily at a dose of 10 mL/kg for 3 consecutive days. The rats were fasted for 12 hours prior to the experiment but had free access to water.

Following deep anesthesia, blood was collected from the abdominal aorta into anticoagulant tubes. The blood samples were centrifuged at 4500 r/minute for 8 minutes, after which 200 μL of the supernatant was transferred into EP tubes. To each tube, 20 μL of hydrochloric acid solution was added, and the mixture was vortexed for 30 seconds, then sonicated in an ice bath for 5 minutes. Next, 780 μL of acetonitrile was added, followed by another vortex for 30 seconds and sonication in an ice bath for 5 minute, before being stored at -40 °C for 30 minutes.

After retrieval, the samples were centrifuged at 12000 r/minute for 15 minutes, and 800 μL of the supernatant was transferred to a new EP tube. This was then centrifuged at low speed to dry the sample, after which 80 μL of extraction solution (methanol: Acetonitrile: water in a 2:2:1 ratio) was added. The mixture was vortexed for 30 seconds, sonicated in an ice-water bath for 1 minute, and centrifuged at 12000 r/minute for 15 minutes. The upper supernatant was collected for testing. For the K group serum samples, 25 μL of the supernatant was set aside to mix into a quality control sample for testing[10].

Analysis of blood constituents by liquid chromatography-tandem mass spectrometry: Chromatography: Target compounds were separated on a Vanquish ultra-high performance liquid chromatography system (Thermo Fisher Scientific, United States) equipped with a Phenomenex Kinetex C18 column (2.1 × 100 mm, 2.6 μm). The mobile phase consisted of solvent A (water with 0.01% acetic acid) and solvent B (isopropanol/acetonitrile, 1:1, v/v). The autosampler temperature was set to 4 °C, and 2 μL was injected for each run[11].

Mass spectrometry: Full-scan first-stage mass spectrum (MS1) and tandem mass spectrum (MS/MS) data were acquired using an Orbitrap Exploris 120 mass spectrometer controlled by Xcalibur (Thermo Fisher Scientific, v4.4, United States). The instrument settings included a sheath gas flow of 50 Arb, an auxiliary gas flow of 15 Arb, and a capillary temperature of 320 °C. Full-scan mass spectrometry spectra were collected at a resolution of 60000, while MS/MS spectra were collected at a resolution of 15000. Stepped normalized collision energies of 20/30/40 were applied, and spray voltages were set to 3.8 kV in positive mode and -3.4 kV in negative mode.

Data processing: Raw files were converted to mzXML format using ProteoWizard. Subsequently, metabolite annotation was performed using an in-house collaboratively developed R package in conjunction with the BiotreeDB database (v3.0). Visualization of the results was created with a custom R workflow. To better understand the annotated plasma features, we cross-referenced the reported constituents of the six herbs in YCWLS with data from Traditional Chinese Medicine Systems Pharmacology Database, Herb Ingredients’ Targets Database, and Public Chemistry Database. We found no exact overlaps between our plasma-borne features and the curated prototype-ingredient entries. This lack of correspondence is likely due to the fact that many circulating signals are biotransformation products and that Metabolomics Standards Initiative level 2-4 annotations do not provide definitive one-to-one matches.

Animal experiments

Animal grouping, modeling and administration: After one week of acclimatization, 30 SPF-grade SD male rats were randomly assigned to either the control group or the experimental group using a random number table. The control group, consisting of 9 rats, was fed a standard diet. In contrast, the experimental group, which included 21 rats, received a HFD composed of 78% basal diet, 20% lard, and 2% cholesterol[12]. After 10 weeks of feeding, 3 rats from each group were randomly selected for tissue analysis. The assessment of liver function, blood lipids, and hematoxylin and eosin-stained liver tissue (HE)-stained liver tissue showed significantly elevated liver function and blood lipid levels in the experimental group compared to the control group. HE staining revealed pronounced hepatocyte swelling and substantial lipid droplet accumulation in the cytoplasm, confirming the successful model establishment. Following this, rats in the experimental group were randomly assigned to one of three groups using a random number table: The model group, the YCWLS (equivalent dose of YCWLS) group, and the CBLT group, with 6 rats in each group. The sample size of six rats per group (n = 6/group) was determined based on previous similar studies and considerations of feasibility. This sample size is commonly utilized in rat experiments involving HFD-induced MAFLD. Starting in week 11, continuous administration was carried out for 4 weeks at a dose of 10 mL/kg once daily. The control and model groups received an equivalent volume of distilled water. According to the equivalent dose ratio for human-to-rat body surface area conversion, as outlined in pharmacological experimental methods, the CBLT dosage was set at 2.1 g/kg, while the YCWLS dosage was established at 6.38 g/kg. Rats were deeply anesthetized by intraperitoneal injection of 3% sodium pentobarbital (30 mg/kg). Euthanasia was then performed in accordance with institutional guidelines.

Weighing of body mass, liver tissue and calculation of liver index: The hair color, activity levels, feces, food intake, and water consumption of the rats were observed daily, while their weights were measured weekly. After the experiment, the rats were weighed again before being deeply anesthetized, and their livers were harvested within 5 minutes. To ensure a complete wet-heavy liver was obtained, the gallbladder attached to the liver and any residual blood in the main blood vessels were removed during the extraction process. The wet weight of the whole liver was weighed using a calibrated electronic balance with an accuracy of 0.01 g. The liver index was calculated using the formula: Liver index = (liver wet weight g/weight before euthanasia g) × 100%.

Hepatic histopathology: Following the administration of anesthesia, an incision was made to access the abdominal cavity, where the liver was excised and subsequently weighed. Tissue samples from the left lobe were fixed in 40 g/L formaldehyde, paraffin-embedded, sectioned to a thickness of 4 μm, stained with HE, and examined under a light microscope for histopathological evaluation.

Detection of liver function and blood lipids: Blood was drawn from the abdominal aorta into heparinized tubes and then centrifuged at 2000 r/minute for 15 minutes to collect the plasma. The plasma samples were analyzed for AST, ALT, TC, TG, HDL-C, and LDL-C using an automated biochemical analyzer.

16S rRNA sequencing: Fresh rat fecal samples were collected aseptically into sterile centrifuge tubes, quickly snap-frozen in liquid nitrogen, and stored at -80 °C until further processing. From the fecal material, genomic DNA was isolated and subsequently diluted with sterile water to a final concentration of 1 mg/L. The V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified using the primer pair 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’). Purify, elute, and quantify polymerase chain reaction-amplified products. Perform PE250/PE300 sequencing using Illumina’s NovaSeq 6000/NextSeq 2000 platform. Raw data were initially assembled and filtered to obtain valid sequences. Next, operational taxonomic unit (OTUs) and feature tables were generated using Vsearch software (version 2.22.1) with 97% similarity-based OTU clustering. In parallel, QIIME2 software (version 2022.8) was used to perform DADA2 denoising on the sequences, resulting in adaptive servo-ventilations (ASVs) and feature tables. Following this, species taxonomic annotation was carried out on the OTU or ASV sequences using the RDP classifier (version 2.13), with a confidence threshold of 0.7 and the Silva 138.1 reference database for bacteria. α-diversity metrics, such as Chao1, Shannon, and Simpson, along with β-diversity measures, including Bray-Curtis distance, were calculated based on the ASV table. Intergroup differences were tested using permutated multivariate analysis of variance with 999 permutations; if hierarchical/batch factors were present, stratified permutations were applied. Differential microbial communities were identified using linear discriminant effect size (α = 0.05, linear discriminant analysis score > 2.0), combined with multiple testing correction methods (e.g., Bejamin-Hochberg false discovery rate), maintaining a default significance set at 0.05[13]. Sequencing and analysis were performed by Suzhou Panomic Biotechnology Co., Ltd. Experimental data were uploaded to the NCBI database (accession No. PRJNA1210116).

Targeted metabolomics analysis of SCFAs: Immediately after euthanasia, fresh fecal samples (approximately 100 mg) were collected and placed into pre-chilled EP tubes. The samples were snap-frozen in liquid nitrogen and stored at -80 °C, with efforts made to avoid repeated freeze-thaw cycles. For sample preparation, 100 mg of feces was weighed into a 1.5 mL centrifuge tube. Then, 500 μL of water and 100 mg of glass beads were added. The mixture was homogenized for 1 minute and centrifuged at 12000 r/minute for 10 minutes at 4 °C. Following centrifugation, 200 μL of the supernatant was collected. Subsequently, 100 μL of 15% phosphoric acid, 20 μL of 4-methylvaleric acid (375 μg/mL; internal standard), and 280 μL of diethyl ether were added sequentially. After homogenizing for 1 minute, the samples were centrifuged again at 12000 r/minute for 10 minutes at 4 °C. The organic supernatant was then collected for subsequent analysis, where SCFAs were separated and quantified using gas chromatography-mass spectrometry. Chromatographic conditions were set according to references[14,15]: A Thermo Trace 1300 gas chromatography system (Thermo Fisher Scientific, United States) equipped with an Agilent HP-INNOWAX capillary column (30 m × 0.25 mm ID × 0.25 μm). Mass spectrometric conditions followed references[14,15]: A Thermo ISQ 7000 mass spectrometer (Thermo Fisher Scientific, United States) with an electron impact source operated in selected ion monitoring mode at an electron energy of 70 eV. External standard curves were established for acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, pentanoic acid, and hexanoic acid. The linear ranges for acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, and pentanoic acid were from 0.02 to 500.0, while hexanoic acid had a linear range of 0.03 to 375.0 (units determined by standard solutions). The linear regression correlation coefficients (r) for each compound ranged from 0.9913 to 0.9969. The retention times (RT) for each target compound were as follows: Acetic acid at 4.60 minutes, propionic acid at 5.68 minutes, isobutyric acid at 6.09 minutes, butyric acid at 6.97 minutes, isovaleric acid at 7.64 minutes, valeric acid at 8.77 minutes, and caproic acid at 10.24 minutes. The calibration curve equations were: Acetic acid: Y = 0.002473x + 0.001865 (r = 0.9932), propionic acid: Y = 0.004652x + 0.0008083 (r = 0.9913), isobutyric acid: Y = 0.006934x + 0.0002982 (r = 0.9969), butyric acid: Y = 0.0145x + 0.0002633 (r = 0.9965), isovaleric acid: Y = 0.01688x + 0.0008303 (r = 0.9954), valeric acid: Y = 0.01756x + 0.0002443 (r = 0.9956), hexanoic acid: Y = 0.04522x + 0.00415 (r = 0.9923). The method limit of quantification was 0.02 (acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid) and 0.03 (hexanoic acid). Here, Y represents (peak area/peak area ratio), and X denotes the concentration of the standard solution.

Statistical analysis

All statistical analyses were performed using GraphPad Prism (v9.0) and SPSS (v26.0). Continuous data are presented as the mean ± SD. Before proceeding with further analyses, data were evaluated for normal distribution using the Shapiro-Wilk test and for equality of variances with Levene’s test. When the data met the assumptions of normality and homogeneity of variance, one-way analysis of variance was performed, followed by Tukey’s post hoc multiple comparisons. If the variance was unequal but the data approximated normal distribution, Welch’s analysis of variance was applied, along with appropriate multiple comparison corrections for intergroup comparisons. In cases where the data did not meet normality, the Kruskal-Wallis test was performed, followed by Dunn’s multiple comparisons for post-hoc analysis. Alongside P values, effect sizes (η²) were reported, including 95% confidence intervals when applicable. Two-tailed tests were conducted, with P < 0.05 denoting statistical significance.

RESULTS

Identification of plasma components from YCWLS

To investigate plasma components after YCWLS administration, Y, K, and YWD were subjected to MS/MS analysis under the conditions specified. Results are shown in Figure 2, presenting total ion chromatograms in both positive and negative ion modes. In the comparison of K, YWD, and Y, a total of 104 plasma-bound compounds were identified in K rat plasma. Among these, 22 were identified as parent compounds, while the remaining 82 were likely metabolites (Table 1). These metabolites were categorized into 32 types of compounds, including flavonoids, coumarins, and tyrosine-based alkaloids. Parent compounds are defined as drug components that enter the bloodstream unchanged or with minimal alteration, whereas metabolites are products formed from the metabolism of these compounds after they have entered the bloodstream.

Figure 2 Illustration of the base peak ion chromatograms of plasma in both positive and negative ion modes. A and B: The Yinchen Wuling San plasma sample; C and D: Indicates the blank plasma sample; E and F: Shows the Chinese medicine extract sample. A, B, and C are analyzed in negative ion mode. D, E, and F are analyzed in positive ion mode. Y1: The Yinchen Wuling San samples group; K1: The drug-containing serum group; YWD: The blank serum group.

Table 1 Identification of blood components in Yinchen Wuling San via ultra-high performance liquid chromatography-tandem mass spectrometry (top 30).

English name	Formula	MZMED	RTMED	MZPPM	Adduct
L-histidine	C6H9N3O2	155.0700431	94.11205	6.874707376	[M]+
Diosmin	C28H32O15	591.1671031	270.3345	6.287430271	[M-H2O+H]+
Citronellyl acetate	C12H22O2	237.1230112	95.1271	8.901819088	[M+K]+
Synephrine	C9H13NO2	150.0910279	128.6115	2.005831636	[M-H2O+H]+
3,5,5-trimethyl-4beta-hydroxy-4-[3-(beta-D-glucopyranosyloxy)-1-butenyl]-2-cyclohexene-1-one	C19H30O8	387.2001167	292.777	3.055977289	[M+H]+
4-methylcyclohexanol	C7H14O	115.1114776	698.5315	1.931686223	[M+H]+
Raffinose	C18H32O16	543.1303366	99.9993	3.417948475	[M+K]+
5-[(Z)-14-(3,5-dihydroxyphenyl)tetradec-10-enyl]benzene-1,3-diol	C26H36O4	430.2945952	419.392	1.544505078	[M+NH4]+
3-amino-2-oxopropyl phosphate	C3H8NO5P	170.0190785	71.55605	12.4780473	[M+H]+
Piscerythramine	C26H29NO6	452.2095794	260.426	6.367520943	[M+H]+
Glutaric acid	C5H8O4	153.0163698	105.836	3.627350757	[M+Na-2H]-
2-hydroxy-6-(4-hydroxy-2-methoxy-6-methoxycarbonyl-phenoxy)-4-methyl-benzoic acid	C17H16O8	348.0799644	368.895	14.51705114	[M]-
Rosmarinic acid	C18H16O8	419.0980201	286.017	0.710820202	[M+CH3COO]-
4-vinylphenol	C8H8O	118.0408611	264.935	13.16392975	[M-2H]2-
9-hydroxy-1,2,11,15,15-pentamethyl-5-(4,5,6-trihydroxy-6-methylheptan-2-yl)-6-oxapentacyclo[8.8.0.02,7.05,7.011,16]octadecan-14-one	C30H50O6	505.3555925	461.842	4.140567905	[M-H]-
Butyl (S)-3-hydroxybutyrate [arabinosyl-(1->6)-glucoside]	C19H34O12	453.1918418	288.4025	13.14703991	[M-H]-
2-phenyllactic acid	C9H10O3	165.0554395	309.942	1.687130633	[M-H]-
4,8-dihydroxy-4a-methyl-7-propan-2-yl-2,3,4,5,6,7,8,8a-octahydronaphthalen-1-one	C14H24O3	239.1650858	358.499	0.895579869	[M-H]-
2-[3-hydroxy-5-(2-hydroxyethyl)phenoxy]-6-(hydroxymethyl)oxane-3,4,5-triol	C14H20O8	315.1083127	207.883	0.911803197	[M-H]-
Eucalyptin	C19H18O5	325.1076596	398.125	1.662114327	[M-H]-
Neotenone	C19H14O6	337.0745352	364.9755	8.114509277	[M-H]-
3-[3-[3,4,5-trihydroxy-6-(hydroxymethyl)oxan-2-yl]oxybutyl]-3H-2-benzofuran-1-one	C18H24O8	367.1380078	299.96	5.153920718	[M-H]-
1-methyl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole-3-carboxylic acid + hydroxy (RP1)	C13H14N2O3	307.1265085	249.29	9.226811951	[M+CH3COO+2H]+
Capillartemisin_B-2H (RP1)	C19H22O4	353.1182522	383.275	9.302870377	[M+K]+
Ganoderic_acid_Mf-2H (RP1)	C32H46O5	510.339525	450.301	10.77817249	[M]+
Camphor + hydroxy (RP2)	C10H16O2	229.1410249	291.012	12.67707909	[M+CH3COO+2H]+
Ephedroxane + hydroxy (RP1)	C11H13NO3	230.0807749	297.288	8.39529186	[M+Na]+
Alnustone + hydroxy (RP1)	C19H18O2	339.1562425	420.569	9.840014502	[M+CH3COO+2H]+
N-feruloylputrescine + glucuronide (RP1)	C20H28N2O9	440.1843138	308.2965	12.1162193	[M]+
Darutigenol + sulfate (RP1)	C20H34O6S	805.4321369	419.14	11.97043215	[2M+H]+

Specifically, among the metabolites that met the criteria for blood entry (P < 0.05 and fold change ≥ 1.2), those that corresponded to TCM components were classified as progenitors, while the others were designated as metabolites. For the identification of compounds, we utilized the Metabolomics Standards Initiative levels as follows: Level 1 indicates that the metabolites in the sample matched both MS1 and second-stage mass spectrum (MS2) of the standard compound and its RT. Level 2 indicates that the metabolites matched both MS1 and MS2 of a public database. Level 3 signifies that the metabolites matched both MS1 and MS2 of a theoretical database along with the predicted RT. Level 4 refers to unknown compounds[16].

Effects of YCWLS on liver-body weight ratio, liver function, and lipid metabolism in HFD rats

As shown in Figure 3A and B, there were no significant differences in body weight among the groups. However, the liver index in the model group was significantly higher than in the control group (P < 0.01), indicating aggravated liver injury. Following treatment, both CBLT and YCWLS significantly reduced the liver index compared to the model group (P < 0.01), with YCWLS showing a greater improvement.

Figure 3 The effect of Yinchen Wuling San on body weight, hepatic function, and fat buildup in metabolic-associated fatty liver disease rats (n = 6). A: Changes in the body mass of the rats in each group; B: Liver index; C: Alanine aminotransferase; D: Aspartate aminotransferase; E: Total cholesterol; F: Triglycerides; G: Low-density lipoprotein cholesterol; H: High-density lipoprotein cholesterol. The data are shown as the mean ± SD. ^bP < 0.01 vs control, ^dP < 0.01 vs model. YCWLS: Yinchen Wuling San; CBLT: Bifidobacterium quadruple live bacteria tablets; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; TC: Total cholesterol; TG: Triglycerides; LDL-C: Low-density lipoprotein cholesterol; HDL-C: High-density lipoprotein cholesterol.

Figure 3C and D indicate that the model group exhibited significantly elevated levels of AST and ALT compared to the control group (P < 0.01), which is consistent with impaired hepatic function. Treatment with either CBLT or YCWLS significantly reduced AST and ALT levels relative to the model group (P < 0.01), with YCWLS showing the most pronounced effect.

As presented in Figure 3E-H, the model group had higher levels of TG, TC, and LDL-C, along with lower HDL-C compared to the control group (P < 0.01), suggesting disordered lipid metabolism. After intervention, both CBLT and YCWLS significantly decreased TG, TC, and LDL-C levels while increasing HDL-C compared to the model group (P < 0.01).

As shown in Figure 4, the HE staining results revealed that the control group had normal liver tissue structure, characterized by regular cell morphology and the absence of significant lipid vacuoles. In contrast, the model group demonstrated disrupted liver tissue structure, irregular cell morphology, and clusters of lipid vacuoles of varying sizes. Both the YCWLS and CBLT groups demonstrated improvements in liver tissue structure and reductions in fatty degeneration when compared to the model group.

Figure 4 Impact of Yinchen Wuling San on lipid buildup in metabolic-associated fatty liver disease rats. YCWLS: Yinchen Wuling San; CBLT: Bifidobacterium quadruple live bacteria tablets.

Effects of YCWLS on gut microbiota diversity in HFD-induced rats

We performed 16S rRNA sequencing and analysis on the feces of rats in each group. As shown in Figure 5A, in the Venn diagram, the control group had a total of 682 ASVs, among which 19 were unique ASVs; the model group had 652 ASVs, with 9 unique ASVs; the YCWLS group had 685 ASVs, including 3 unique ASVs; and the CBLT group had 631 ASVs, with 5 unique ASVs. As shown in Figure 5B and C, the rarefaction curves and Shannon-Wiener curves flattened at the ends, suggesting that the sequencing data volume in this study was reasonable and the sequencing depth was reliable, which could effectively reflect the diversity of the microbiota in the samples.

Figure 5 The impact of Yinchen Wuling San on the diversity of the intestinal microbiota in rats (n = 6). A: Venn diagram; B: Rarefaction curve; C: Shannon-Wiener curve; D: Observed; E: Chao1; F: Ace; G: Shannon; H: Simpson; I: Pielou; J: Analysis of similarity analysis control vs model; K: Analysis of similarity analysis Yinchen Wuling San vs model; L: Analysis of similarity analysis Bifidobacterium quadruple live bacteria tablets vs model; M: Posterior communicating artery analysis. ^aP < 0.05 vs control, ^bP < 0.01 vs control, ^cP < 0.05 vs model, ^eP < 0.05 vs Yinchen Wuling San, ^fP < 0.01 vs Yinchen Wuling San. YCWLS: Yinchen Wuling San; CBLT: Bifidobacterium quadruple live bacteria tablets.

As shown in Figure 5D-I, the model group exhibited significantly lower Chao1, observed, and Ace indices compared to the control group (P < 0.01). In contrast, the Shannon, Simpson, and Pielou indices showed no statistically significant differences (all P > 0.05). When compared to the model group, the YCWLS group demonstrated significantly increased Chao1, observed, and Ace indices (P < 0.05), while the Shannon, Simpson, and Pielou indices remained statistically insignificant (P > 0.05). These findings suggest that HFD treatment significantly reduces gut microbiota richness and diversity, whereas YCWLS therapy markedly enhances gut microbiota diversity in MAFLD rats.

Figure 5J-L illustrates the use of analysis of similarity, a nonparametric test that compares intergroup differences to intragroup differences. It categorizes intergroup differences as large (r > 0.75), moderate (r > 0.5), or small (r > 0.25), with P < 0.05 indicating significant intergroup differences. This test evaluates the meaningfulness of grouping by assessing differences in community structure. The credibility of the results is indicated by the P value, which is calculated using the permutation test. This test randomly shuffles samples into groups, computes the permuted R-value (denoted as Ri), and after N permutations (≥ 1000), determines the probability that Ri exceeds the original R value, designated as p. A P < 0.05 signifies statistical significance. In this analysis, the control group exhibited moderate intergroup differences compared to the model group (r = 0.443, P = 0.004). The model group also showed moderate differences when compared to the YCWLS group (r = 0.356, P = 0.002) and to the CBLT group (r = 0.491, P = 0.003). As shown in Figure 5M, posterior communicating artery analysis revealed significant differences in gut microbiota community composition between the model and control rats. After the YCWLS intervention, the overlap in gut microbiota composition between the YCWLS and model rats decreased.

Effects of YCWLS on gut microbiota composition in HFD-induced rats

As shown in Figure 6A and B, Firmicutes and Bacteroidetes were the predominant phyla across all groups, together accounting for over 90% of the microbiota, while Verrucomicrobia and Actinobacteria were also detected. The model group exhibited a significantly higher abundance of Firmicutes compared to the control group (P < 0.01), alongside a reduction in Bacteroidetes. In contrast, YCWLS treatment resulted in a significant decrease in Firmicutes (P < 0.05) and a notable increase in Verrucomicrobia (P < 0.01) compared to the model group. As shown in Figure 6C-G, the top 10 genera by relative abundance were evaluated. Relative to the control group, the model group showed increased Ruminococcaceae and Clostridiales and decreased Lachnospiraceae and Peptostreptococcaceae. After YCWLS administration, Ruminococcaceae and Clostridiales tended to decline, whereas Lachnospiraceae, Peptostreptococcaceae, Oscillospira, and Akkermansia increased. In the CBLT group, Clostridiales decreased significantly (P < 0.01) while Peptostreptococcaceae increased significantly (P < 0.01).

Figure 6 The effect of Yinchen Wuling San on the rat intestinal microbiota at the phylum and genus levels (n = 6). A and B: Represent the relative abundances at the phylum level; C and D: Indicate the relative abundances at the genus level; E: Wilcoxon rank - sum test control vs model; F: Wilcoxon rank - sum test Yinchen Wuling San vs model; G: Wilcoxon rank - sum test CBLT vs model. ^bP < 0.01 vs control, ^cP < 0.05 vs model, ^dP < 0.01 vs model. YCWLS: Yinchen Wuling San; CBLT: Bifidobacterium quadruple live bacteria tablets.

As illustrated in Figure 7, linear discriminant effect size analysis evaluated the impact of significantly different biological features using nonparametric Kruskal-Wallis and Wilcoxon rank-sum tests, with a threshold set for linear discriminant analysis at > 2. The YCWLS group identified 33 key differential factors, while the model group identified only 1. Notably, genera such as Akkermansia, Oscillospira, Coriobacteriaceae, Christensenellaceae, and Corynebacterium were significantly enriched in the YCWLS group. In contrast, the CBLT group identified 27 key differentiating factors, which included significant enrichment of genera such as Akkermansia, Staphylococcus, Jeotgalicoccus, and Enterococcus. Meanwhile, the model group identified 5 key differentiating factors.

Figure 7 Examination of the fundamental microbial composition in the intestinal contents of rats. A: Model vs Yinchen Wuling San; B: Model vs Bifidobacterium quadruple live bacteria tablets. YCWLS: Yinchen Wuling San; CBLT: Bifidobacterium quadruple live bacteria tablets; LDA: Linear discriminant analysis.

Effects of YCWLS on SCFAs levels in rats induced by HFD

As shown in Table 2, the levels of fecal acetate, propionate, butyrate, and valerate in the model group were generally lower than those in the controls, although these differences were not statistically significant (P > 0.05). Compared with the YCWLS group demonstrated an upward trend in acetate, propionate, isobutyrate, butyrate, isovalerate, and valerate, yet these increases also lacked statistical significance (P > 0.05). Similarly, the CBLT group displayed a trend of increasing levels for several SCFAs, but again, none of these changes reached significance (P > 0.05).

Table 2 Effects of Yinchen Wuling San on intestinal short-chain fatty acids in rats (n = 6), mean ± SD.

Indicator (μg/g)	Control (n = 6)	Model (n = 6)	YCWLS (n = 6)	CBLT (n = 6)	F value	P value	Effect size(η2)
Acetic	1368.33 ± 182.89	1360.36 ± 408.36	1620.63 ± 230.69	1467.54 ± 465.96	0.749	0.536	0.10
Propionic	445.04 ± 66.09	451.29 ± 114.76	521.80 ± 78.19	475.62 ± 183.33	0.509	0.681	0.07
Isobutyric	67.17 ± 10.69	69.11 ± 13.82	79.71 ± 6.32	73.83 ± 30.12	0.599	0.623	0.08
Butyric	434.14 ± 104.89	423.88 ± 69.45	471.64 ± 99.79	316.71 ± 94.09	3.064	0.052	0.31
Isovaleric	49.32 ± 8.57	51.34 ± 12.01	59.52 ± 5.15	56.20 ± 28.81	0.478	0.701	0.07
Valeric	71.48 ± 10.88	63.45 ± 17.00	82.95 ± 12.28	70.42 ± 31.56	1.008	0.41	0.13
Caproic	13.19 ± 6.82	6.56 ± 8.74	5.73 ± 4.25	2.88 ± 3.81	2.937	0.058	0.31

YCWLS: Yinchen Wuling San; CBLT: Bifidobacterium quadruple live bacteria tablets.

Correlation analysis of characteristic bacterial genera with liver function, blood lipids, and SCFAs

To investigate the correlation between characteristic bacterial genera in rats and various biomarkers (AST, ALT, TC, TG, HDL-C, and LDL-C), we screened for genera using species markers. This process led us to identify eight genus-level bacteria: Jeotgalicoccus, Ruminococcus, Oscillospira, Akkermansia, Corynebacterium, Turicibacter, Desulfovibrio, and Anaerostipes. We then intersected these with the top 30 most abundant genera, resulting in the selection of Oscillospira, Akkermansia, Ruminococcus, Turicibacter, and Desulfovibrio.

We conducted Spearman correlation analysis between these five selected genera and the biomarkers. As illustrated in Figure 8A and B, the abundance of Akkermansia showed a significant positive correlation with HDL-C (P < 0.01) and significant negative correlations with AST, ALT, TC, TG, and LDL-C (P < 0.01). Similarly, Turicibacter abundance exhibited a significant positive correlation with HDL-C (P < 0.05) and a significant negative correlation with TG (P < 0.05). Additionally, Oscillospira abundance showed a significant negative correlation with ALT (P < 0.01).

Figure 8 Correlation analysis among specific bacterial genera, hepatic function, blood lipids, and short-chain fatty acids. A: Correlations between specific bacterial genera and liver function as well as blood lipids; B: Association study between specific bacterial genera and short-chain fatty acids; C: Correlations between specific bacterial genera and liver function as well as blood lipids; D: Correlation study between short-chain fatty acids, hepatic function, and lipid profiles in the bloodstream. AST: Aspartate aminotransferase; ALT: Alanine aminotransferase; TC: Total cholesterol; TG: Triglyceride; LDL: Low-density lipoprotein; HDL: High-density lipoprotein.^aP < 0.05, ^bP < 0.01.

Figure 8C presents the results of the Spearman correlation analysis between the characteristic bacterial genera and SCFAs. The findings indicate that Akkermansia abundance has a significant positive correlation with both valeric acid and propionic acid (P < 0.05). Furthermore, Desulfovibrio shows a significant positive correlation with valeric acid (P < 0.05), while Oscillospira exhibits a significant positive correlation with butyric acid (P < 0.05).

As shown in Figure 8D, the correlation analysis between SCFAs and liver function/lipid profiles revealed that acetate had a significant positive correlation with LDL-C (P < 0.05) and a positive trend with ALT, TC, and HDL-C. Conversely, TG exhibited a negative trend with propionate, isobutyrate, butyrate, and valerate, although these results were not statistically significant. In summary, YCWLS treatment was associated with increased Akkermansia abundance and modest fecal SCFAs trends, which correlated with improvements in liver function and lipid parameters.

DISCUSSION

This study systematically evaluated the efficacy of YCWLS in ameliorating liver injury and lipid metabolism disorders in an HFD-induced MAFLD rat model. By integrating 16S rRNA sequencing with targeted SCFAs detection, we investigated the potential mechanisms via the gut-liver axis. The results demonstrated that YCWLS significantly reduced the liver index and serum ALT/AST levels, improved dyslipidemia (characterized by elevated TC, TG, LDL-C, and decreased HDL-C), and alleviated hepatic steatosis. Furthermore, YCWLS enhanced microbial diversity and restructured the gut microbiota, exhibiting a trend of decreased Firmicutes, increased Verrucomicrobia, and recovery of key genera such as Akkermansia and Oscillospira. Regarding SCFAs, while no statistically significant differences were observed overall, butyrate showed a borderline significant trend (P = 0.052). Correlation analysis revealed significant associations between key bacterial genera and lipid profiles, liver function, and certain SCFAs. Collectively, these findings support the hypothesis that YCWLS may intervene in MAFLD by improving liver injury and enhancing metabolic pathways through microbiota remodeling.

Effects of YCWLS on liver function and lipid metabolism in MAFLD rats

The core pathologies of MAFLD include hepatic lipid accumulation, liver function impairment, and systemic metabolic disorders. In this study, rats in the model group exhibited elevated levels of ALT, AST, TC, TG, and LDL-C, along with decrease in HDL-C. This was accompanied by hepatic steatosis and structural disorganization, indicating that the HFD-induced model effectively replicates MAFLD-related phenotypes. After 4 weeks of YCWLS intervention, there was a reduction in the liver index, a significant drop in ALT and AST levels, marked improvements in lipid profiles, and alleviation of hepatic histopathological damage. These findings align with the research group's previous conclusions[9], confirming that YCWLS effectively repairs MAFLD-related liver damage and regulates lipid metabolism balance. As a classic TCM formula designed to clear heat, drain dampness, strengthen the spleen, and promote diuresis, the effects of YCWLS can be attributed to the synergistic actions of its constituent herbs. For instance, Yinchen contains an ethyl acetate fraction that promotes lipid metabolism, while Poria polysaccharides help reduce TC and further enhance lipid metabolism. Together with other herbs in the formula, they provide hepatoprotective and lipid-lowering benefits[17,18]. Compared with the positive control CBLT, YCWLS exhibited enhanced efficacy in reducing the hepatic index, improving liver function, and regulating the balance of lipid metabolism. These findings suggest that TCM formulas may offer distinct advantages in modulating the pathological progression of MAFLD.

Regulatory effects of YCWLS on gut microbiota dysbiosis in MAFLD rats

Gut microbiota dysbiosis can influence hepatic lipid metabolism and inflammatory responses through the gut-liver axis[5,6]. In comparison to the control group, the model group demonstrated a reduction in microbial richness and alterations in community structure. Following the YCWLS intervention, richness indices, including Chao1, observed, and Ace, showed an increase, indicating a trend toward convergence in community structure with the control group as evidenced by posterior communicating artery and analysis of similarity analyses. This suggests a comprehensive remodeling effect, on the microbiome. At the phylum level, the model group displayed a significant increase in the abundance of Firmicutes coupled with a relative decrease in Verrucomicrobia. After the YCWLS intervention, the abundance of Firmicutes decreased, while Verrucomicrobia increased. At the genus level, a relative decrease in abundance was observed for Ruminococcaceae, Clostridiales, Lactobacillus, Allobaculum, and Coprococcus, whereas Lachnospiraceae, Peptostreptococcaceae, Oscillospira, and Akkermansia showed relative increases. It is important to note that the Firmicutes/Bacteroidetes ratio is frequently employed to characterize microbiota shifts associated with metabolic disorders; however, its directional significance is not uniformly consistent across studies. Therefore, interpretations should be anchored in specific genera rather than relying solely on phylum-level changes for linear causal inferences.

The recovery of Akkermansia deserves special attention. Correlation analysis showed significant positive associations with HDL-C and significant negative correlations with ALT, AST, TC, TG, and LDL-C. This suggests that Akkermansia may be closely linked to the improvement of liver function and lipid metabolism abnormalities in YCWLS. Previous studies have generally identified Akkermansia as a protective microbiota for metabolic diseases, as it regulates metabolism by enhancing the intestinal barrier and producing SCFAs[19]. Its abundance negatively correlates with MAFLD severity[20-22]. Consequently, the upregulation of Akkermansia by YCWLS may be associated with changes in the gut-liver axis, but causal contributions remain to be validated. Furthermore, following the YCWLS intervention, the reduction in Firmicutes abundance leads to a decrease in intestinal lipid absorption. Microbial taxa, including Lachnospiraceae, Oscillospira, Lactobacillus, and Clostridiales, are known to produce SCFAs like butyrate, thereby regulating intestinal inflammation, barrier integrity, and immune responses[23-27]. Although Ruminococcaceae are important microbiota for allergic diseases, their abundance is also significantly increased in obese patients. Therapeutic reduction of Ruminococcaceae abundance can upregulate fibroblast growth factor 21 expression and decrease hepatic TG and TC accumulation[28]. Allobaculum, as an SCFAs producer, exhibits improved metabolic outcomes with increased abundance[29]. This study identified a negative correlation between Allobaculum abundance and disease severity, contradicting previous research findings. This discrepancy may be attributed to the complex dynamics of gut microbiota, which are influenced by multiple factors, including environmental conditions, genetic predispositions, and dietary habits. In the context of disease, the microbiota experiences a state of disruption, resulting in irregular distribution patterns. MAFLD is not a unidimensional condition; its progression is intricately linked to gut microbiota composition, intestinal barrier permeability, endotoxin levels, and chronic inflammatory processes. Consequently, the capacity to concurrently modulate metabolic pathways and gut microbiota may significantly impact overall treatment outcomes. The intervention using YCWLS, which enhances liver function and lipid profiles while simultaneously restructuring microbial structure, offers a biological foundation for its multi-target intervention.

YCWLS regulates gut-liver axis function via the “gut microbiota-SCFAs” axis

SCFAs are essential metabolites generated by the fermentation of dietary fiber by gut microbiota. They play a crucial role in maintaining intestinal barrier function, regulating immunity, and facilitating energy metabolism. As such, SCFAs serve as pivotal molecules linking “microbiome changes” to “host phenotypes”[30-32]. Research indicates[33] that elevating SCFAs levels in obese patients not only helps improve intestinal barrier dysfunction but also promotes optimal balance of gut hormones, thereby suppressing appetite signals. Different SCFAs exert distinct protective mechanisms against MAFLD: Acetate activates the AMP-activated protein kinase pathway, reducing hepatic lipid accumulation and promoting fatty acid oxidation[34]. Propionate enhances intestinal GLP-1 secretion through the activation of G protein-coupled receptor 41/43 (GPR41/43) receptors. This process not only suppresses appetite but also improves insulin resistance[35]. Butyrate, serving as the primary energy source for intestinal epithelial cells, can stimulate GLP-1 secretion[36], enhance intestinal barrier function[37,38], regulate gut-liver axis immune responses[39], and modulate adipose tissue metabolism[40,41]. Valeric acid improves impaired intestinal barrier function by upregulating tight junction proteins (e.g., claudins, E-cadherin, and Zo-1), restoring its integrity, and reducing intestinal permeability and endotoxemia risk[42]. Experimental results indicated that the model group exhibited a downward trend in acetate, butyrate, and valerate levels compared to the control group. After the YCWLS intervention, several SCFAs showed an upward trend; however, these changes did not reach statistical significance, with butyrate showed a trend toward significance (P = 0.052). It is crucial to understand that fecal SCFAs levels do not necessarily reflect systemic SCFAs levels. SCFAs are rapidly absorbed by the colonic epithelium and enter the portal venous circulation. The levels of SCFAs in feces are influenced by multiple factors, including production, absorption, excretion, and intestinal transport. This means that increased actual production may not lead to significant differences in fecal levels.

Despite the lack of overall significance in SCFAs levels, correlation analyses in this study supported the presence of an “axis”: Akkermansia showed significant positive correlations with propionic acid and valeric acid, while Oscillospira had a significant positive correlation with butyric acid. Concurrently, Akkermansia demonstrated directionally consistent correlations with indicators of liver function and lipid profile. This suggests that YCWLS may influence specific SCFAs or their downstream signaling pathways by increasing the abundance of Akkermansia and other potential acid-producing bacteria, which could contribute to improved lipid metabolism and reduced liver injury. Notably, we did not quantify SCFAs receptors (GPR41/43), histone deacetylase (HDAC) activity, or SCFAs transporters [monocarboxylate transporter 1 (MCT1)/sodium-coupled MCT1 (SMCT1)] in gut tissues; therefore, functional activation of SCFAs signaling remains to be confirmed. Based on the current data, we propose that YCWLS establishes an ecological foundation for restoring SCFAs and their receptor signaling through microbiota remodeling; however, further validation is needed to determine whether SCFAs serve as the primary mediating factor.

Research value, limitations, and future directions

This study utilized the MAFLD model while incorporating CBLT as a positive control. As a multi-strain probiotic formulation, CBLT can restore gut microbiota structure and drive SCFAs production to inhibit hepatic lipid accumulation[43]. By employing a comprehensive strategy that integrates microbiome sequencing with targeted analysis of SCFAs, this study systematically characterized the alterations in the microbiome and metabolic products resulting from the YCWLS intervention. The findings furnish experimental evidence for interpreting classical formulas within the context of the “gut-liver axis”. However, several limitations remain: (1) SCFAs differences did not reach statistical significance, potentially related to sample size, intervention duration, detection sites, and absorption/utilization; future studies will aim to increase the sample size or extend the duration of interventions while incorporating measurements of blood and portal vein SCFAs to strengthen the robustness of conclusions; (2) The lack of validation for direct microbial effects through fecal microbiota transplantation (FMT) limits the ability to confirm the causal contributions of core microbiota. Future FMT studies will determine the necessary and sufficient roles of core microbiota, such as Akkermansia, in therapeutic efficacy. Additionally, we cannot exclude microbiota-independent (direct hepatic) effects of YCWLS constituents, which should be disentangled in future studies using microbiota depletion and/or FMT designs; (3) Moreover, indicators related to the intestinal barrier, inflammation, and endotoxins, including lipopolysaccharide, tumor necrosis factor-α, interleukin-1β, interleukin-6, and tight junction proteins, were not included. This omission leaves the relationship in the “microbiota-SCFAs-intestinal barrier-liver” chain at an inferential level; (4) We did not evaluate SCFAs receptors (GPR41/43), SCFAs transporters (MCT1/SMCT1), or HDAC-related signaling in intestinal or hepatic tissues. Consequently, the tissue-level functional activation of SCFAs remains to be validated in future mechanistic studies; and (5) Pathway-specific metabolic validation was not performed. We did not measure hepatic markers of de novo lipogenesis (e.g., acetyl-CoA carboxylase, fatty acid synthase, sterol regulatory element-binding protein 1c), fatty acid oxidation (e.g., carnitine palmitoyltransferase 1A, peroxisome proliferator-activated receptors-α), or AMP-activated protein kinase signaling, nor did we validate farnesoid X receptor-related pathways in this cohort. In addition, we did not assess adipose tissue lipid metabolism, including lipid droplet dynamics and lipolysis-related markers. Therefore, the metabolic basis of YCWLS efficacy requires further mechanistic investigation.

CONCLUSION

This study utilized 16S rRNA gene sequencing combined with SCFAs-targeted metabolomics to systematically evaluate the regulatory effects of YCWLS on the gut microbiota in HFD-induced MAFLD rats, as well as the interrelationship between gut microbiota and host metabolism. The results indicate that YCWLS may correct imbalances in key microbial groups, including Firmicutes, Verrucomicrobia, Peptostreptococcaceae, Lachnospiraceae, Oscillospira, and Akkermansia, contributing to its intervention in MAFLD. This treatment was associated with an overall increase in SCFAs, with butyrate showing a trend toward significance (P = 0.052), and it led to improvements in AST, ALT, and lipid metabolism disorders in MAFLD rats. Overall, this study elucidates the potential mechanism of YCWLS treatment for MAFLD through the lens of “microbiome remodeling”, providing new evidence for the modern interpretation of classical Chinese medicine formulas and providing a scientific basis for the rational clinical application of YCWLS in MAFLD intervention.

ACKNOWLEDGEMENTS

We thank all individuals who provided technical assistance and supported data processing during this study. We also thank the lecturers for their guidance in experimental design and data analysis. In addition, we acknowledge the Animal Ethics and Welfare Committee of Hunan University of Chinese Medicine for its ethical review and oversight of the animal experiments.