Dietary glucoraphenin ameliorates obesity and steatotic liver disease associated with gut dysbiosis induced by a Western diet in mice

Abstract

Western-style diets promote obesity, gut dysbiosis, and metabolic dysfunction-associated steatotic liver disease (MASLD). In this study, glucoraphenin (GRE), the major glucosinolate in radish (Raphanus sativus L.) greens and sprouts, was evaluated for its effects on Western diet (WD)-induced metabolic derangements and gut dysfunction in mice. Intaking of 1 mg/kg body weight of GRE with WD for 8 weeks caused significant reductions in adipogenesis indicators such as body weight gains, adipocyte hypertrophy, and serum leptin level. This result was further proved by significant changes in lipogenesis and lipolysis-related protein expressions in adipocyte. Administration of GRE also suppressed development of MASLD, which was observed through reductions in serum ALT and AST and suppressions the expression of FAS, SREBP1, mTOR, SCD1, ACC, and ChREBP in liver tissues. GRE administration effectively prevented the decrease in the gut microbial richness and diversity caused by WD and recovered serum branched-chain amino acids and microbiota-derived purine metabolites (inosine and hypoxanthine). These findings indicate that GRE mitigates WD-induced obesity and MASLD via coordinated regulation of lipid metabolism and the gut–liver axis.

Introduction

The global prevalence of obesity and metabolic dysfunction-associated steatotic liver disease (MASLD) has been increasing in recent years, posing a major public health problem¹. The rising incidence of these metabolic disorders appears to be closely linked to the widespread adoption of a Western lifestyles and diets rich in fat, sugar, and processed foods². Given these trends, accessible, safe, and effective strategies to prevent or mitigate diet-induced metabolic diseases are urgently needed. Functional food-based strategies, such as herbal extracts and its bioactive compounds, are potentially useful avenues³.

The gut environment plays a crucial role in maintaining systemic metabolic homeostasis, and its disruption has emerged as a key contributor to metabolic diseases⁴. Gut dysbiosis is generally defined as an absence of beneficial microorganisms, an increase in harmful microorganisms, and a decrease in microbial diversity⁵. Western diet (WD) causes gut dysbiosis, compromises intestinal barrier integrity, and exacerbates systemic inflammation, obesity, and metabolic diseases⁶. Thus, restoring a balanced gut microbiota and improving intestinal environment represent promising approaches for preventing metabolic disorders.

Glucoraphenin (GRE), the major glucosinolate abundant in radish greens and sprouts, is hydrolyzed by myrosinase to sulforaphene, a bioactive isothiocyanate⁷. In a previous study, we demonstrated that radish greens exhibited anti-obesity and anti-MASLD effects in a mouse model induced by WD, highlighting its value as a functional food source⁸. Given that GRE is one of the major bioactive components enriched in radish greens, we hypothesized that it could contribute to these metabolic benefits. However, the specific role of GRE in preventing WD-induced obesity and MASLD has not yet been studied.

This study aimed to elucidate the protective mechanism of GRE against WD-induced obesity and gut dysbiosis. The effects of GRE on metabolic biomarkers, hepatic and adipose lipid metabolism, intestinal environment and gut dysbiosis, and metabolomic profiles were evaluated in a mouse model.

Results

Effects of GRE on weight gains and serum leptin level in HFD-induced mice

As expected, the final body weight gain of the WD group mice was significantly increased compared to the normal diet (ND) group (p < 0.001; Fig. 1A). Both the GTE and GRE groups showed significantly lower weight gain compared to the WD group (p < 0.001; Fig. 1A). However, no significant differences in food intake were observed among the three groups (Fig. 1B). WD-fed mice showed significant increases in liver and epididymal white adipose tissue (eWAT) weight and fat mass compared to ND-fed mice (p < 0.05); in contrast, GRE intervention significantly inhibited these indicators (p < 0.05; Fig. 1C‒E). Furthermore, serum leptin level was significantly decreased in the GRE group compared with the WD group (p < 0.05; Fig. 1F).

Fig. 1. Effect of glucoraphenin consumption on obesity-related biomarkers in a Western diet-induced obese mouse model.

All mice were allocated to four groups: normal diet-fed group (ND), Western diet-fed group (WD), WD administered with green tea extract group (GTE), and WD administered with glucoraphenin group (GRE). A Body weight gain for each mouse group. B Food intake during the 8 weeks of feeding. C Liver weights. D White adipose tissue weights. E Representative body composition and fat mass. F Serum leptin levels. The values are the means ± standard error of the mean (n = 9 (A–D and F) or n = 6 (E)). For 1A results, statistical significance was analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test (compared to WD group, * p < 0.05, ** p < 0.01, *** p < 0.001). For 2B-F results, data represented with different letters indicate significant differences between groups (p < 0.05), which were derived by one-way ANOVA followed by Duncan’s multiple range test.

Effects of GRE on adipocyte hypertrophy in HFD-induced mice

Hematoxylin and eosin (H&E) staining results in eWAT showed an approximately 3-fold increase in adipocyte size in the WD group compared to the ND group (Fig. 2A). GRE intervention significantly inhibited WD-induced adipocyte size increase (p < 0.05; Fig. 2A).

Fig. 2. Effect of glucoraphenin consumption on histology and western blot in epididymal white adipose tissue (eWAT) of a Western diet-induced obese mouse model.

All mice were allocated to four groups: normal diet-fed group (ND), Western diet-fed group (WD), WD administered with green tea extract group (GTE), and WD administered with glucoraphenin group (GRE). A Hematoxylin and eosin staining results using eWAT for each mouse group. B Western blotting results using eWAT to detect the expressions of lipid metabolism-related proteins. The values are the means ± standard error of the mean (n = 3). Data represented with different letters indicate significant differences between groups (p < 0.05), which were derived by one-way ANOVA followed by Duncan’s multiple range test.

Effects of GRE on obesity-related protein expression of adipose tissue in HFD-induced mice

As shown in Fig. 2B, the expression levels of lipid synthesis-related proteins anti-acetyl-CoA carboxylase (ACC), anti-sterol regulatory element-binding protein 1 (SREBP1), anti-stearoyl-CoA desaturase 1 (SCD1), and anti-fatty acid synthase (FAS) in eWAT of the WD group were significantly higher than the ND group (p < 0.05), while the protein expression levels treated with GRE were significantly lower than the WD group (p < 0.05). In addition, the expression of anti-adipose triglyceride lipase (ATGL), a lipolysis-related protein, was significantly reduced in WD-fed mice compared to the ND-fed mice (p < 0.05). GRE administration elevated ATGL expression compared to the WD group (p < 0.05), indicating enhanced lipolysis by GRE administration.

Effects of GRE on suppressing MASLD-related serum biomarkers in HFD-induced mice

Effects of GRE on the serum biomarker levels of WD-fed mice were investigated after the end of the experiment (Fig. 3A, B). Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in the WD group were significantly higher compared to the ND group (p < 0.05). However, administration of GRE led to a significant decrease in the levels of ALT and AST (p < 0.05).

Fig. 3. Effect of glucoraphenin consumption on histology and western blot in liver tissues of a Western diet-induced obese mouse model.

All mice were allocated to four groups: normal diet-fed group (ND), Western diet-fed group (WD), WD administered with green tea extract group (GTE), and WD administered with glucoraphenin group (GRE). A Serum alanine aminotransferase levels. B Serum aspartate aminotransferase levels. C Triglyceride contents in liver tissues for each mouse group. D Oil red O staining results using liver tissues. E Hematoxylin and eosin staining results using liver tissues. F Western blotting results using liver tissues to detect the expressions of lipogenesis-related proteins. The values are the means ± standard error of the mean (n = 9 (A–C) or n = 3 (D–F)). Data represented with different letters indicate significant differences between groups (p < 0.05), which were derived by one-way ANOVA followed by Duncan’s multiple range test.

Effects of GRE on hepatic steatosis in HFD-induced mice

Hepatic triglyceride contents were notably accumulated in the WD group compared to the ND group but were significantly decreased by GTE and GRE treatments (p < 0.05; Fig. 3C). GRE significantly inhibited WD-induced lipid accumulation in liver tissues, as assessed by Oil red O (ORO) staining (p < 0.05; Fig. 3D). These effects were also associated with significant reductions in steatosis, inflammation, hepatocyte ballooning, and MASLD activity score (MAS) (Fig. 3E). Remarkably, the GRE group suppressed the increase in MAS induced by WD to a level similar to that of the positive control group, the GTE group (p < 0.05; Fig. 3E).

Effects of GRE on MASLD-related protein expression of hepatic tissue in HFD-induced mice

To further investigate the benefits of GRE in suppressing hepatic steatosis, we analyzed the expression levels of lipid synthesis-related proteins in liver tissues (Fig. 3F). WD-fed mice exhibited significantly increased expression of FAS, SREBP1, anti-mammalian target of rapamycin (mTOR), SCD1, ACC, and anti-carbohydrate-responsive element-binding protein (ChREBP) compared to the ND group (p < 0.05), while the protein expression levels treated with GRE were significantly lower than the WD group (p < 0.05).

Effects of GRE on gut dysbiosis in HFD-induced mice

Histological analysis confirmed suppressing mucin production in the colon tissues of WD-fed mice, as evidenced by decreased alcian blue-stained area, which was significantly increased by GTE and GRE intervention (p < 0.05; Fig. 4A). To investigate the effects of GRE on the gut environments, we analyzed the composition of the gut microbiota by performing 16S rRNA amplicon sequencing on feces collected at week 8 (Fig. 4B‒D). Alpha diversity analysis (Shannon and ASVs), which reflects the richness and diversity of species, showed that WD intake significantly reduced the index levels (p < 0.05; Fig. 4B). However, GRE intervention significantly restored the WD-induced diversity reduction (p < 0.05). Principal Coordinate Analysis (PCoA) based on weighted UniFrac distances revealed that the WD group was clearly distinct from the ND group (Fig. 4C). In contrast, the GRE group exhibited changes in the gut microbial composition similar to the ND group. Taxonomic analysis at the phylum level revealed that the WD-induced increase in Bacillota and decrease in Bacteroidota were notably reversed by GRE intervention (Fig. 4D). At the genus level, the fecal microbial composition revealed differences between the ND and WD groups as well as between the WD and GRE groups (Fig. 4E, F). In the ND group, several genera including Limosilactobacillus, Muribaculum, Lactobacillus, Sangeribacter, Bacteroides, Paramuribaculum, Parabacteroides, Schaedlerella, and Desulfovibrio were significantly enriched; in contrast, the WD group was characterized by an increased abundance of Enterococcus and Adlercreutzia (Fig. 4E). Notably, intake with GRE resulted in a distinct enrichment pattern compared with the WD group (Fig. 4F).

Fig. 4. Effect of glucoraphenin consumption on histology in colon tissues and microbial composition of a Western diet-induced obese mouse model.

All mice were allocated to four groups: normal diet-fed group (ND), Western diet-fed group (WD), WD administered with green tea extract group (GTE), and WD administered with glucoraphenin group (GRE). A Alcian blue staining results using colon tissues. B Alpha diversity index chart in fecal samples. C Principal Coordinates Analysis results using the Weighted UniFrac distance between samples. D Relative abundance of colon microflora at phylum levels. E, F Linear discriminant analysis (LDA) bar plots showing differentially abundant genera identified by LEfSe analysis between the ND and WD groups (E) and between the WD and GRE groups (F) (Kruskal–Wallis test, p < 0.05; LDA score > 2.0). The values are the means ± standard error of the mean (n = 3 (A) or 3 cages (B–F; three mice per cage)). Data represented with different letters indicate significant differences between groups (p < 0.05), which were derived by one-way ANOVA followed by Duncan’s multiple range test.

Effects of GRE on fecal and serum metabolites in HFD-induced mice

To determine the effect of GRE on the metabolite changes in mice, we analyzed 45 metabolites in fecal and serum samples (Fig. 5). In fecal samples, WD intake significantly increased most amino acids (p < 0.05), whereas GRE intervention reversed these changes, but not significantly (Fig. 5A). Interestingly, GRE administration significantly reversed the reduction of isoleucine, proline, trans-4-hydroxyproline, valine, inosine, and hypoxanthine in serum samples (p < 0.05; Fig. 5B). In contrast, analysis using the false discovery rate (FDR) method showed a decrease in the number of metabolites with significant differences between groups (Figure S1A, B). In the GRE vs WD comparison, leucine was the only metabolite that remained significant after FDR correction, showing an increase in the GRE group (q < 0.05; Figure S1B).

Fig. 5. Effect of glucoraphenin consumption on metabolites in feces and serum of a Western diet-induced obese mouse model.

All mice were allocated to four groups: normal diet-fed group (ND), Western diet-fed group (WD), WD administered with green tea extract group (GTE), and WD administered with glucoraphenin group (GRE). Heat maps comparing WD group in A fecal metabolites and B serum metabolites (n = 9 (A) or 3 cages (B; three mice per cage)). *p < 0.05, **p < 0.01, and ***p < 0.001 when compared to ND group. #p < 0.05 and ##p < 0.01 when compared to WD group.

Discussion

High-fat and high-carbohydrate intake not only contributes to the development of metabolic diseases, including obesity and MASLD, but also disrupts gut barrier function and gut microbial homeostasis, leading to systemic inflammation⁹. There is growing interest in developing effective and safe ingredients to prevent metabolic diseases, and radish greens is one potential natural product that may help alleviate these conditions. Our previous study demonstrated that radish greens attenuates WD-induced obesity and MASLD⁸. Radish greens contains various bioactive phytochemicals, including flavonoids, phenolic acids, and glucosinolates⁷. Radish sprouts have been reported to contain substantial levels of glucosinolates, with glucoraphenin among the predominant components¹⁰. In addition, our phytochemical analysis showed that GRE was present in radish green extract at 1.32 mg/g (dry weight basis; data not shown). Glucosinolates are hydrolyzed by the enzyme myrosinase and converted into isothiocyanate, which has biological activity¹¹. In this study, we aimed to elucidate the efficacy of GRE, one of the glucosinolates.

GRE, abundant in radish greens, is converted by myrosinase into sulforaphene, which has anticancer, anti-aging, and anti-inflammatory effects^12–14. However, the efficacy of GRE on diet-induced obesity or MASLD has not yet been studied. Glucoraphanin, which differs from GRE only in its side chain structure, has been reported to have anti-obesity properties¹⁵. While glucoraphanin is converted to sulforaphane when hydrolyzed by myrosinase, GRE is converted to sulforaphene, an isothiocyanate with an unsaturated side chain¹¹. Despite the structural similarity, the functional significance of GRE is not identical to that of glucoraphanin, highlighting the need for this study. This study is the first to investigate the effectiveness of GRE in preventing obesity and MASLD due to WD. To administer high-quality glucosinolates, GRE was purchased and used with a purity of 98% or higher, rather than being extracted from radish greens.

In this study, the GRE dosage was determined to be 1 mg/kg based on previous studies of other glucosinolates (1.5 or 7.5 mg/kg for 3,3′-diindolylmethane and 2.5 mg/kg for glucoraphanin)^16,17, and the GRE content in the radish greens extract was not considered. Many animal models have been used to study the effects of obesogenic environments, particularly the WD, known as high-fat and high-carbohydrate diets^18,19. In this study, when WD was fed to mice for 8 weeks, increases in body weight, fat mass, liver weight, and eWAT weight, which are representative indicators of obesity, were observed. GRE administration reduced these WD-induced obesity-related indicators and attenuated the WD-induced elevation of serum ALT, AST, and leptin levels. Additionally, the reduction level was similar to the GTE group used as a positive control. ALT and AST are markers of liver damage, and their elevated levels are considered surrogate markers of hepatic steatosis²⁰. The hormone leptin has also been linked to metabolic disorders, including obesity, type 2 diabetes, and MASLD, and has been shown to have a positive correlation with hepatic steatosis in particular²¹. These results suggest that GRE improves WD-induced obesity and may be associated with the alleviation of hepatic steatosis.

Excess substrates from WD activated de novo lipogenesis (DNL), leading to lipid accumulation in adipose tissue and liver tissue²². Histological analysis of eWAT observed that GRE administration inhibited WD-induced adipocyte size expansion. GRE also affected DNL pathway in eWAT by suppressing the expression of ACC, SREBP1, SCD1, and FAS, which were increased by WD. WD consumption increased the expression of SREBP1, which activates genes related to fatty acid synthesis, in adipose tissue²³. Acetyl-CoA produced by SREBP1 is converted to malonyl-CoA by ACC, which is then converted to fatty acids by FAS and SCD1²⁴. Various fatty acids and glycerol 3-phosphate are converted to triglycerides, leading to lipid accumulation; our results demonstrated that GRE administration is involved in DNL and suppresses lipid accumulation induced by WD. In contrast, ATGL is a key enzyme in lipolysis, hydrolyzing triglycerides in adipose tissue into free fatty acids and diacylglycerol²⁵. GRE administration significantly increased ATGL expression in WD-induced mice, indicating activation of lipolysis. These results demonstrated that GRE suppresses WD-induced lipid accumulation in eWAT by inhibiting DNL and increasing lipolysis.

MASLD, which encompasses a broad spectrum of liver pathologies, can progress to fibrosis or cirrhosis, thereby underscoring the need for early treatment and prevention⁹. Having previously indirectly demonstrated the association between GRE administration and the improvement of fatty liver disease through serum analysis, we sought to elucidate the mechanism through liver tissue staining and western blot analysis. GRE administered mice have shown to not only inhibit WD-induced hepatic triglyceride accumulation and hepatic steatosis, but also inhibit the MAS, which reflects the degree of steatosis, hepatocyte ballooning, and inflammation. As mentioned in the eWAT results above, administration of GRE in WD-induced mice significantly reduced the expression of FAS, SREBP1, SCD1, and ACC within the DNL pathway. mTOR and ChREBP, like SREBP1 described above, are key transcriptional regulators that induce DNL promoting enzymes in the liver²⁶. SREBP1 activated by insulin or mTOR signaling, and ChREBP activated by glucose, activate genes related to fatty acid synthesis²⁷. We showed that GRE administration significantly suppressed the expression levels of proteins involved in DNL increased by WD intake. These data indicate that GRE exerts hepatoprotective effects against MASLD by inhibiting the DNL pathway activated by WD. Furthermore, as the MAS suggested inflammatory alterations in WD-fed mice, future studies should determine whether GRE modulates hepatic inflammatory responses during MASLD progression.

WD negatively affects the gut microbiota and immune system by causing gut dysbiosis, impaired gut barrier function, and leakage of harmful bacterial metabolites into the bloodstream, ultimately leading to obesity and metabolic syndrome²⁸. Alcian blue staining of colon tissues showed decreased mucus secretion in WD-induced mice, suggesting impaired gut barrier integrity²⁹. Conversely, GRE administration inhibited the gut barrier integrity damage caused by WD, and the degree of mucin staining was similar to that of the positive control group, the GTE group. Previous studies have reported that reduced mucin production is associated with disrupted barrier function, and defects in the mucus layer can promote endotoxin translocation^29,30. Therefore, although circulating endotoxin and inflammatory cytokines were not measured in this study, the recovery of mucin suggests a potential improvement in gut barrier integrity. Furthermore, our results showed that 8 weeks of GRE administration effectively prevented the decrease in the gut microbial richness and diversity caused by WD, thereby suppressing gut dysbiosis, one of the hallmarks of obesity³¹. In particular, the GRE group showed an increased abundance of several genera, including Eubacterium, Parabacteroides, and Bacteroides, compared to the WD group. These genera have been previously reported to be associated with metabolic regulation or intestinal homeostasis in experimental and clinical studies^32–34. Recent studies have shown that glucosinolates influence the gut microbiome and have significant positive effects on human health³⁵. The gut microbiome can significantly influence the bioavailability of glucosinolate derivatives, and in particular, glucoraphanin was demonstrated to be hydrolyzed to sulforaphane by cecum microbiota of rats, leading to systemic absorption³⁶. Our findings suggest that gut microbiota-mediated hydrolysis of GRE to sulforaphene influences obesity and metabolic diseases, but further studies are needed to clarify these interactions. Additionally, further studies supplementing GRE to healthy controls will be needed to elucidate the effects of GRE on gut microbiota.

Nutritional metabolomics provides a mechanism to study the impact of dietary patterns on chronic metabolic diseases using biochemical pathways³⁷. In addition, multi-omics approaches may help understand the complex pathways of glucosinolates derived from bacterial metabolism in the complex human gut environment³⁵. We analyzed the metabolites of feces and serum to determine the effects of GRE on WD-induced mice. While WD-induced metabolomic changes were greater in feces than in serum, the effects of GRE administration were more evident in serum samples. The lack of significant changes in fecal metabolites despite clear alterations in serum profiles may reflect the inherently higher variability of the fecal metabolome compared with circulating metabolites, as fecal metabolic composition is strongly influenced by microbial and environmental factors³⁸. In contrast, circulating metabolites generally exhibit greater metabolic stability and stronger correlations with host physiology³⁹. Therefore, these findings suggest that systemic metabolic readouts may more sensitively reflect the physiological impact of GRE rather than indicating an absence of intestinal effects. In the serum results, isoleucine, proline, trans-4-hydroxyproline, valine, inosine, and hypoxanthine, which were significantly decreased by WD, were significantly recovered by GRE administration. There is a correlation between circulating levels of branched-chain amino acids, including isoleucine and valine, and diet-induced obesity, suggesting that GRE administration could restore metabolic homeostasis of amino acids⁴⁰. Proline-hydroxyproline, a collagen-derived bioactive dipeptide, have been reported to be associated with gut microbiota, particularly Bacteroidales strains, and to promote mucin synthesis and gut barrier function⁴¹. Moreover, inosine and hypoxanthine are metabolites derived from gut microbiota and have been demonstrated to improve the integrity and function of the mucosal barrier, respectively^42,43. These results suggest that GRE administration restored metabolites associated with metabolic disorders and intestinal barrier impairment induced by WD; however, the present findings remain associative rather than causal. Further studies employing approaches such as fecal microbiota transplantation or targeted metabolite supplementation will be required to determine whether microbiota-derived metabolites directly mediate the physiological effects of GRE. Furthermore, FDR analysis revealed few significant changes due to GRE administration, necessitating further study.

In the present study, we demonstrate that GRE administration can protect against WD-induced obesity and MASLD by suppressing body weight and fat mass gain, adipocyte hypertrophy, hepatic steatosis, and elevation of serum biomarkers. GRE attenuates DNL caused by WD and enhances lipolysis by regulating protein expression levels in eWAT and liver tissues. In addition, GRE administration can improve the intestinal environment by promoting mucin production and preventing gut dysbiosis. GRE promotes the restoration of metabolites associated with WD-induced metabolic dysfunctions and gut barrier damage. Our findings suggest that GRE supplementation may help mitigate metabolic disorders associated with a Western-style diet.

Methods

Chemicals and reagents

GRE (purity ≥ 98%, 2514S) was purchased from Extrasynthese Co. (Lyon Nord, France). The ND (2918C) and the WD (TD.88137; consisting of 42.7% kcal from carbohydrate and 42.0% kcal from fat, formula: sucrose 341.46 g/kg, andydrous milkfat 210.0 g/kg, casein 195.0 g/kg, corn starch 150.0 g/kg, and cholesterol 1.5 g/kg, etc.) were purchased from Envigo RMS Inc. (Indianapolis, IN, USA). Mouse & rat leptin ELISA kit (MOB00B) was purchased from R&D Systems (Minneapolis, MN, USA) and triglyceride colorimetric assay kit (10010303) was purchased from Cayman Chemical (Ann Arbor, MI, USA). Tissue protein extraction reagent (78510) was purchased from Thermo Fisher Scientific (Waltham, MA, USA), and phosphatase inhibitors (P5726 and P0044) were purchased from Sigma-Aldrich (St. Louis, MO, USA). ACC (ab45174), SREBP1 (ab191857), FAS (ab22759), and ATGL (ab207799) were purchased from Abcam (Cambridge, UK). SCD1 (2794), anti-β-actin (4967), mTOR (2983), ChREBP (58069), and anti-glyceraldehyde-3-phosphate dehydrogenase (GAPDH; 5174) were purchased from Cell Signaling Technology (Danvers, MA, USA).

Animals and treatments

Animal protocol was performed according to the criteria approved by the Korea Food Research Institute Animal Care and Use Committee (KFRI-M-24034). The experiments were conducted following prescribed animal research guidelines. For this study, thirty-six male C57BL/6N mice (5-weeks-old, purchased from Koatech Co., Pyeongtaek, Republic of Korea) were acclimated for one week, where they were housed under controlled conditions and received 2918C. After the acclimation period, animals were randomly divided into four groups (n = 9 per group and three mice per cage) as follows. ND group received a ND for eight continuous weeks. The second group continuously received the WD diet and was named the WD group. The third group received this same WD and administered 100 mg catechin/kg of green tea extract, and was named the GTE group. The fourth group received WD and administered 1 mg/kg of GRE, and was named the GRE group. The GTE group was used as a positive control to compare the efficacy of GRE. Simultaneously with the feeding, the ND and WD groups were orally administered vehicle (distilled water), and the GTE and GRE groups were orally administered a solution of each sample diluted in distilled water. All the oral administration was provided to the mice daily, and the body weight and food intake were measured once per week during the 8-week period. After 8 weeks of treatment, mice were anesthetized with isoflurane, and blood, liver, eWAT, and colon samples were collected for biomarker analysis.

For serum analysis, ALT and AST levels were calculated using a Hitachi 7180 automatic analyzer (Hitachi, Tokyo, Japan). Serum leptin levels were measured using a leptin ELISA kit following the manufacturer’s protocol. For hepatic triglyceride analysis, triglyceride colorimetric assay kit was used according to the manufacturer’s protocol.

Histopathological analysis

Paraffin sections of eWAT and liver from three randomly selected mice per group were stained with H&E for histopathological analysis. In addition, frozen hepatic slices were stained with ORO, and paraffin section of colon was stained with alcian blue. Representative photomicrographs were captured using a digital slide scanner (MoticEasyScan Pro, Motic, Xiamen, China). Adipocyte size, ORO-stained area, and alcian blue-stained area were quantified using ImageJ (NIH, Bethesda, MD, USA). H&E staining results of liver tissues were assessed using the MAS, a semiquantitative measure of steatotic liver disease activity, which corresponds to the former NAFLD activity score (the sum of steatosis, lobular inflammation, and hepatocyte ballooning (Table 1)⁴⁴.

Table 1.

Metabolic dysfunction-associated steatotic liver disease (MASLD) activity score system

Item	Score	Extent
Steatosis	0	<5%
	1	5–33%
	2	34–66%
	3	>66%
Lobular Inflammation	0	No foci
	1	<2 foci per 200× field
	2	2–4 foci per 200× field
	3	>4 foci per 200× field
Hepatocyte Ballooning	0	None
	1	Few balloon cells
	2	Many cells/prominent ballooning

Western blot analysis

Total protein from the eWAT and liver from three randomly selected mice per group was extracted with protein extraction reagent supplemented with phosphatase inhibitors. The protein samples (20 μg) were separated by 4–20% SDS-PAGE and transferred to PVDF membranes. The membranes were blocked with blocking buffer and incubated with the primary and secondary antibodies. The protein bands were detected using a ChemiDoc XRS+ imaging system (Bio-Rad, Hercules, CA, USA). β-actin and GAPDH served as internal references to ensure equal loading of samples.

16S rRNA amplicon sequencing

Fecal samples were collected at 8 weeks from three cages per group (three mice per cage), microbial genomic DNA was isolated, and the 16S rRNA V3–4 region was sequenced using the Illumina MiSeq platform (Macrogen, Seoul, Republic of Korea). Briefly, after sequencing, adapter and primer sequences were removed from the raw data, and forward and reverse reads were trimmed to 250 bp and 200 bp using Cutadapt (v3.2)⁴⁵. To generate ASV sequences, the reads were performed error-correction and denoising processes with DADA2 (v1.18.0)⁴⁶, and sequences with an expected errors of 2 or more were excluded. Following error correction, paired-end reads were merged by overlapping, and ASVs with lengths shorter than 350 bp were filtered out using R (v4.0.3). Taxonomic classification was performed against the NCBI_16S reference database using a Bayesian classifier implemented in DADA2 (confidence value: 50). For microbial community comparison analysis, normalization was performed using QIIME (v1.9.0), and the resulting ASVs were utilized for downstream analysis⁴⁷. Shannon index was calculated to represent species complexity within individual samples, and also presented ASV, which represents the number of different species. The mafft and FastTreeMP were used to perform multiple alignment and construct phylogenetic tree of ASVs^48,49. Beta diversity was calculated based on Weighted UniFrac distances⁵⁰, and genetic relationship among samples was visualized based on PCoA. The genus-level profiles of the gut microbiota were analyzed using linear discriminant analysis effect size (LEfSe) to identify differentially abundant taxa compared to the WD group. To reduce the influence of sparse features and to satisfy the assumptions of non-parametric statistical testing, genera detected in fewer than two samples per group were excluded prior to LEfSe analysis. Statistical significance was assessed using the non-parametric Kruskal–Wallis test among groups, followed by pairwise Wilcoxon rank-sum tests. Linear discriminant analysis (LDA) was then applied to estimate the effect size of differentially abundant genera.

Metabolomics analysis

Metabolite profiling was performed as previously described⁵¹. Briefly, fecal samples (50 mg) were extracted with 50% methanol and sonicated to enhance metabolite release. The homogenate was centrifuged, and the supernatants were filtered through a PTFE syringe filter. Serum samples (20 μL) were subjected to protein precipitation by adding acetonitrile. The mixtures were centrifuged, and the supernatants were collected. Metabolites were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Analytical conditions followed a previously described protocol. A targeted panel of metabolites was quantified in serum (n = 9 per group) and fecal samples (n = 3 per group). Quantification was performed using internal standards (phenylalanine-13C915N, lysine-13C615N2, leucine-13C615N, cholic acid-d4, and carnitine-d9).

Statistical analysis

All the data are presented as mean ± standard error of the mean (SEM) using GraphPad Prism 10.5.0 (GraphPad Software, Inc.), and statistically analyzed using SPSS software v20 (IBP Corp.). Intragroup differences were analyzed via one-way ANOVA followed by Duncan’s multiple range test, and p < 0.05 indicated a statistical significance. For body weight results, statistical significance was analyzed using two-way ANOVA followed by Tukey’s multiple comparisons test (GraphPad Software, Inc.). LEfSe analyses were performed in R (v4.5.2) using the microbiomeMarker package, and LDA effect size plots were generated using ggplot2 for data visualization (p < 0.05, LDA score > 2.0). For metabolite analysis results attached in the supplementary materials, metabolite-wise p-values were adjusted for multiple comparisons using the Benjamini–Hochberg FDR procedure, and FDR-adjusted q-values are reported.

Author contributions

H.-B.L.: Data curation, formal analysis, methodology, writing–original draft. Y.R.L.: Data curation, formal analysis. H.-J.K.: Formal analysis, methodology. I.C.: Writing–review & editing. M.P.: Formal analysis. S.W.J.: Resources, investigation. J.-H.Y.: Formal analysis. H.-Y.P.: Conceptualization, supervision, methodology, writing–review & editing. All authors reviewed the manuscript.

Data availability

The metabolomics and microbiome data have been deposited to the Zenodo data repository: https://doi.org/10.5281/zenodo.18712723.

Competing interests

The authors declare no competing interests.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1038/s41538-026-01000-5.