Abstract

   Matcha shows promise for diabetes, obesity, and gut microbiota
   disorders. Studies suggest a significant link between gut microbiota,
   metabolites, and obesity. Thus, matcha may have a positive impact on
   obesity by modulating gut microbiota and metabolites. This study used
   16S rDNA sequencing and untargeted metabolomics to examine the cecal
   contents in mice. By correlation analysis, we explored the potential
   mechanisms responsible for the positive effects of matcha on obesity.
   The results indicated that matcha had a mitigating effect on the
   detrimental impacts of a high-fat diet (HFD) on multiple physiological
   indicators in mice, including body weight, adipose tissue weight, serum
   total cholesterol (TC), and low-density lipoprotein (LDL) levels, as
   well as glucose tolerance. Moreover, it was observed that matcha had an
   impact on the structural composition of gut microbiota and gut
   metabolites. Specifically, matcha was able to reverse the alterations
   in the abundance of certain obesity-improving bacteria, such as
   Alloprevotella, Ileibacterium, and Rikenella, as well as the abundance
   of obesity-promoting bacteria Romboutsia, induced by a HFD.
   Furthermore, matcha can influence the levels of metabolites, including
   formononetin, glutamic acid, pyroglutamic acid, and
   taurochenodeoxycholate, within the gastrointestinal tract.
   Additionally, matcha enhances caffeine metabolism and the HIF-1
   signaling pathway in the KEGG pathway. The results of the correlation
   analysis suggest that formononetin, theobromine, 1,3,7-trimethyluric
   acid, and Vitamin C displayed negative correlation with both the
   obesity phenotype and microbiota known to exacerbate obesity, while
   demonstrating positive correlations with microbiota that alleviated
   obesity. However, glutamic acid, pyroglutamic acid, and
   taurochenodeoxycholate had the opposite effect. In conclusion, the
   impact of matcha on gut metabolites may be attributed to its modulation
   of the abundance of Alloprevotella, Ileibacterium, Rikenella, and
   Romboutsia within the gastrointestinal tract, thereby potentially
   contributing to the amelioration of obesity.

   Keywords: Matcha, Obesity, High-fat diet, Gut microbiota, Untargeted
   metabolomics, Correlation analysis

Graphical abstract

   Image 1
   [51]Open in a new tab

Highlights

     * •
       Matcha can alleviate obesity caused by a high-fat diet.
     * •
       Matcha can change the species composition and structure of gut
       microbiota.
     * •
       Matcha can regulate the composition of gut metabolites.
     * •
       Matcha by regulating gut microbiota and gut metabolites to
       alleviate obesity.

1. Introduction

   Obesity is one of the major health problems endangering human health in
   the world. Obesity currently affects more than 2 billion people
   worldwide, and the prospects for controlling the obesity epidemic are
   not good ([52]Caballero, 2019). In addition to many metabolic diseases
   such as diabetes ([53]Ng et al., 2021), cardiovascular and
   cerebrovascular diseases ([54]Kim et al., 2021), fatty liver
   ([55]Hashem et al., 2021), and chronic kidney disease ([56]Vasylyeva
   and Singh, 2016), obesity can also lead to reproductive disorders
   ([57]Crujeiras and Casanueva, 2015) and mental diseases such as
   depression ([58]Milaneschi et al., 2019). Thus, there is an urgent need
   to address the health crisis of obesity.

   Matcha, a powder derived from green tea leaves, is commonly utilized in
   the food realm. In addition to its unique flavor profile, matcha has
   shown promise in possessing anti-inflammatory, anti-diabetic, and
   anti-obesity characteristics ([59]Ye et al., 2023). Studies have shown
   that matcha has the potential to mitigate weight gain, elevated blood
   lipids, and liver damage caused by a high-fat diet (HFD) in mice (J.
   [60]Zhou et al., 2021). In addition, matcha can also inhibit JAK2/STAT3
   signaling pathway to prevent obesity-induced hypothalamic inflammation
   (J. [61]Zhou et al., 2020). A recent prospective study has suggested
   that incorporating matcha into one's diet may result in notable
   decreases in body weight, body mass index, waist circumference, and
   fasting blood glucose levels in individuals who are obese
   ([62]El-Elimat et al., 2022). Nevertheless, the specific mechanism by
   which matcha alleviates obesity remains to be further explored.

   The gut microbiota, also known as the “second genome”, is distinguished
   by its dynamic and variable composition and function, which can
   interact with dietary components to extensive impact various
   physiological processes in the host ([63]Schoeler and Caesar, 2019; B.
   [64]Zhu et al., 2010). Among them, the host energy metabolism and
   balance is considered to be important targets for the role of gut
   microbiota ( Cani and Van Hul, 2024). Gut microbiota and their
   metabolites play an important role in the pathogenesis of obesity and
   related diseases (M. [65]Zhou et al., 2023a). Specifically, the gut
   microbiota has been shown to regulate metabolic pathways such as
   branched-chain amino acid metabolism and bile acid metabolism in the
   gut, thus playing a significant role in the onset and progression of
   obesity ([66]Allegretti et al., 2020; [67]Miyamoto et al., 2019;
   [68]Zeng et al., 2020). Recent studies have shown that green tea can
   change the gut microbiota to promote thermogenesis, as well as prevent
   or improve obesity (D. [69]Li et al., 2023a; [70]Tian et al., 2024).
   Matcha served as a powder of green tea processing, and it has also been
   suggested that the supplementation of matcha may help alleviate
   alterations in stool bile acid composition and gut microbiota caused by
   a HFD (Y. [71]Wang et al., 2022b). Consequently, the gut microbiota and
   gut metabolites are believed to be significant factors in the potential
   efficacy of matcha in addressing obesity.

   This research utilized a HFD to induce obesity in a mouse model and
   administered matcha via gavage to assess its effects on obesity. Then,
   16S rDNA sequencing and untargeted metabolomics analysis were employed
   to investigate the impact of matcha on gut microbiota and metabolites.
   Subsequently, correlation analysis was conducted to explore the
   metabolic interactions between gut microbiota and metabolites, with the
   goal of elucidating the potential mechanism by which matcha may
   mitigate obesity.

2. Materials and methods

2.1. Animal feeding and sample collection

   The C57BL/6J male mice (6 weeks old) were provided by Beijing Vital
   River Laboratory Animal Technology Co., Ltd. All the animal experiments
   were performed in accordance with the requirements of the Laboratory
   Animal-Guideline for ethical review of animal welfare. And the animal
   experiments were approved by the Animal Ethics Committee of Southwest
   Medical University (No. swmu20220181). The mice were housed in the
   specific pathogen-free facility (SPF) barrier system at the
   Experimental Animal Center of Southwest Medical University, with an
   ambient temperature of 22 ± 2 °C, humidity of 50%–60%, and a 12-h
   dark/light cycle. The experimental procedures complied with the ethical
   guidelines for laboratory animal welfare. After one week of adaptation,
   30 mice were randomly divided into a control group (CK group, n = 10)
   and a high-fat diet group (HFD group, n = 20). The CK group was fed a
   normal diet, while the HFD group was fed a high-fat diet (HFD). The
   normal diet contained carbohydrates 65.08 kcal%, protein 23.07 kcal%,
   and fat 11.85 kcal%; the high-fat diet contained carbohydrates
   20 kcal%, protein 20 kcal%, and fat 60 kcal%. Detailed compositions of
   the normal and HFD diets are provided in [72]Supplement Table 1. During
   the experiment, all experimental animals ate and drank freely. In the
   12th week, mice in the HFD group were randomly divided into a model
   control group (MK group, n = 10) and a matcha group (M group, n = 10).
   Over the following 5 weeks, mice in the M group were orally
   administered matcha physiological saline solution (1 g/kg body weight,
   provided by Sichuan University of Science and Engineering), while mice
   in the CK and MK groups were orally administered an equivalent amount
   of physiological saline. Detailed compositions of the matcha is
   provided in [73]Supplement Table 1. In the 16th week, 4 mice from each
   group were randomly selected for glucose tolerance tests and insulin
   tolerance tests. The remaining mice continued to be orally administered
   their respective solutions until the 17th week. This was to avoid the
   potential impact of intraperitoneal glucose and insulin injection on
   16S rDNA sequencing and untargeted metabolomics results. At the end of
   week 17, these 6 mice were fasted overnight, euthanized by cervical
   dislocation under 1% pentobarbital sodium anesthesia (50 mg/kg body
   weight), and immediately collected and weighed the peritesticular
   adipose tissue. The contents of the cecum below the cecum valve were
   collected, frozen at −80 °C, and used for 16S rDNA sequencing and
   untargeted metabolomics analysis.

2.2. Glucose tolerance test (GTT) and insulin tolerance test (ITT)

   In the 16th week, four randomly selected mice from each group were
   fasted for 12 h. Blood samples were collected via tail vein puncture,
   and blood glucose concentrations were measured using a blood glucose
   meter (Roche, ACCU-CHEK). Subsequently, 10% glucose solution (2 g/kg
   body weight) was injected intraperitoneally. Blood glucose
   concentrations were then measured at 30, 60, 90, and 120 min after
   administration. After the experiment, mice resumed their original diet
   and oral administration. In the 17th week, the aforementioned randomly
   selected mice were fasted for 4 h, and blood glucose concentrations
   were measured using the same method. Subsequently, insulin solution at
   a dose of 0.75 IU/kg was injected intraperitoneally at a concentration
   of 0.0075 IU/ml. Blood glucose concentrations were measured at 30, 60,
   90, and 120 min after administration.

2.3. Biochemical analysis of serum

   Blood samples were collected in the morning and centrifuged at 4 °C at
   3500 r/min for 10 min. Serum was collected, and the concentrations of
   testing triglyceride (TG), total cholesterol (TC), low-density
   lipoprotein (LDL), and high-density lipoprotein (HDL) were measured
   using a fully automatic veterinary biochemical analyzer (Jiangxi Tekang
   Technology Co., Ltd., TC220).

2.4. Fat histopathology

   The peritesticular adipose tissue was immersed in 4% paraformaldehyde
   for fixation, followed by washing, dehydration, paraffin embedding,
   sectioning (4 μm), and staining with hematoxylin-eosin. The tissue
   samples were evaluated for histopathological characteristics under a
   microscope, with three photographs taken for each sample. The area of
   adipocytes in the sample images was measured and analyzed using Image
   Pro Plus 6.0.

2.5. 16S rDNA sequencing

   The gut microbial genomic DNA from the cecal contents was extracted
   using the HiPure Stool DNA Kit (D3141, Guangzhou Meiji Biological Co.,
   LTD., China). The V3-V4 region of the 16S rDNA (341F:
   CCTACGGGRBGCASCAG; 806R: GGACTACNNGGGTATCTAAT) was amplified using
   specific primers with barcode. The amplified products were recovered,
   quantified, purified, and used for library construction and subsequent
   sequencing. After obtaining raw reads, low-quality reads were filtered
   using FASTP (V0.18.0, [74]https://github.com/OpenGene/fastp), followed
   by joining reads into tags using FLASH (V1.2.11,
   [75]http://www.cbcb.umd.edu/software/flash), filtering and removing
   chimeras from tags to obtain effective tags. OTU abundance was
   calculated based on effective tags. The sequence information of OTUs
   was compared with the SILVA database to obtain species annotation
   information for each OTU. Alpha diversity analysis was conducted using
   QIIME (V1.9.1, [76]http://qiime.sourceforge.net/), while beta diversity
   analysis was performed using R studio. The LEfSe software was employed
   for Linear discriminant analysis effect size (LEfSe) analysis (V1.0,
   [77]http://huttenhower.sph.harvard.edu/lefse/), with species having an
   LDA value > 4 considered as biomarkers.

2.6. Untargeted metabolomics

   The cecal contents were slowly thawed at 4 °C, and an appropriate
   amount of sample was added to a solution of methanol/acetonitrile/water
   (2:2:1, v/v), mixed, and left at −20 °C for 10 min. Subsequently, the
   mixture was centrifuged at 14000g, 4 °C for 20 min, and the supernatant
   was taken for analysis by LC-MS. An equal volume of each sample was
   mixed to create a pooled sample used as a quality control (QC). Samples
   were analyzed using Ultra-High Performance Liquid Chromatography
   (UHPLC, 1290 Infinity LC, Agilent Technologies) with a HILIC column for
   separation (Column temperature: 25 °C, flow rate: 0.5 mL/min, sample
   volume:2 μL). The mobile phase contained A: 25 mM ammonium acetate and
   25 mM ammonium hydroxide in water and B: acetonitrile. The gradient
   elution program was as follows: 0–0.5 min, 95% B; 0.5–7 min, linear
   decrease of B from 95% to 65%; 7–8 min, linear decrease of B from 65%
   to 40%; 8–9 min, B held at 40%; 9–9.1 min, linear increase of B from
   40% to 95%; 9.1–12 min, B held at 95%. Throughout the analysis, samples
   were kept in an autosampler at 4 °C. Samples were analyzed
   consecutively in random order, with QC samples inserted into the sample
   queue. AB Triple TOF 6600 mass spectrometer was used to collect the
   primary and secondary spectra of the samples. The ESI source conditions
   after HILIC chromatographic separation were as follows: Ion Source Gas1
   (Gas1): 60, Ion Source Gas2 (Gas2): 60, Curtain gas (CUR): 30, source
   temperature: 600 °C, IonSpray Voltage Floating (ISVF) ±5500 V; TOF MS
   scan m/z range: 60–1000 Da, product ion scan m/z range: 25–1000 Da, TOF
   MS scan accumulation time 0.20 s/spectra, product ion scan accumulation
   time 0.05 s/spectra; second-level mass spectrometry was conducted using
   information dependent acquisition (IDA) and high sensitivity mode,
   Declustering potential (DP): ±60 V, Collision Energy: 35 ± 15 eV, IDA
   settings as follows: Exclude isotopes within 4 Da, Candidate ions to
   monitor per cycle: 10. Peak identification, filtering, and alignment
   were performed on the mass spectra to obtain data results including
   mass-to-charge ratio, retention time, and peak area. Metabolites were
   annotated using databases such as Mass Bank, Metlin, and MoNA.

2.7. Statistical analysis

   In this study, we performed statistical analysis using SPSS 25.0
   software. The Shapiro-Wilk test was employed to assess the normality of
   the data. For normally distributed data, ANOVA test was conducted,
   while the Kruskal-Wallis rank sum test was used for non-normally
   distributed data. Spearman correlation analysis was utilized to assess
   the correlation between different types of data samples. GraphPad Prism
   9.0.0 was used to calculate the area under the curve. P < 0.05
   indicated a statistically significant difference between the
   experimental results.

3. Results

3.1. Matcha can alleviate obesity-related phenotypes in mice

   Following a 12-week period of consuming a high-fat diet (HFD), compared
   to CK mice, the HFD mice exhibited a significant increase in body
   weight ([78]Fig. 1A, P < 0.05). Upon commencement of matcha gavage
   treatment, the body weight of M mice continued to decrease ([79]Fig.
   1B). The body weight of M mice was significantly lower than MK mice
   from the 15th week ([80]Fig. 1B, P < 0.05). And during the period of
   matcha gavage treatment, there was no significant difference in food
   intake and energy intake between M and MK mice ([81]Supplement Figs.
   1a–b). After a high-fat diet, the adipose tissue weights of MK mice
   were significantly higher than that of CK mice ([82]Fig. 1C, P < 0.05).
   However, after matcha supplementation, the adipose tissue weight of M
   mice was significantly lower than that of MK mice ([83]Fig. 1C,
   P < 0.05). Adipose tissue weight as a percentage of body weight showed
   the same results ([84]Fig. 1D, P < 0.05). These results suggest that
   the supplementation of matcha may have the potential to counteract
   weight gain and adipose tissue accumulation induced by a HFD.
   Additionally, compared to CK mice, the mean adipocyte area of MK mice
   showed a significant increase (P < 0.05), whereas the mean adipocyte
   area of M mice did not differ significantly from that of MK and CK mice
   ([85]Fig. 1E and F).

Fig. 1.

   [86]Fig. 1
   [87]Open in a new tab

   Effect of matcha on obesity-related phenotypes in high-fat diet
   (HFD)-induced obese mice. (A) Body weight of mice during the obesity
   model induced by high fat diet. (B) Effect of matcha on body weight,
   (C) adipose tissue weight, (D) adipose tissue weight as a percentage of
   body weight, (E) size of adipocytes under HE staining, (F) average
   adipocyte area, (G, I) glucose tolerance test (GTT) and (H, J) insulin
   tolerance test (ITT), and (K) serum lipid profile including
   triglyceride (TG), total cholesterol (TC), high-density lipoprotein
   (HDL), and low-density lipoprotein (LDL). Different lowercase letters
   indicate significant differences between groups (P < 0.05), and data
   are expressed as mean ± SD (n = 6), *P < 0.05 and **P < 0.01.

   Furthermore, obesity induced by a HFD has been shown to negatively
   impact glucose and insulin tolerance in mice. To assess this, glucose
   tolerance tests (GTT) and insulin tolerance tests (ITT) were conducted
   at weeks 4 and 5 of supplementation. In the GTT, compared to CK mice,
   the blood glucose levels of MK mice were increased significantly. But
   the blood glucose levels of M mice were significantly lower than that
   of MK mice ([88]Fig. 1G, P < 0.05). However, no significant differences
   were observed among the three groups in the ITT ([89]Fig. 1H).
   Additionally, the area under the curve in the GTT showed that MK mice
   had significantly larger values than CK and M mice ([90]Fig. 1I,
   P < 0.05). However, the analysis of the area under the curve in ITT
   revealed no statistically significant difference between the groups of
   mice ([91]Fig. 1J). This suggests that while matcha may have a
   significant impact on alleviating the impaired glucose tolerance of
   mice, its effect on enhancing insulin sensitivity is not statistically
   significant. In comparison to CK mice, MK mice exhibited significantly
   elevated serum levels of triglycerides (TG), total cholesterol (TC),
   high-density lipoprotein (HDL), and low-density lipoprotein (LDL)
   (P < 0.05). Conversely, following supplementation of matcha, compared
   to MK mice, M mice demonstrated significantly reduced serum levels of
   TC, HDL, and LDL ([92]Fig. 1K, P < 0.05). These results indicate that
   matcha supplementation can effectively mitigate HFD induced increases
   in body weight, adipose tissue weight, and lipid levels, as well as
   enhance glucose tolerance.

3.2. Effect of matcha on gut microbiota composition in mice

3.2.1. Alpha and beta diversity

   In the Alpha diversity analysis, the ACE, Chao1, and Shannon indices of
   MK mice were found to be significantly lower than those of CK mice
   ([93]Fig. 2A, P < 0.05). However, compared to MK mice, the ACE, Chao1,
   and Shannon indices of M mice did not exhibit significant differences
   ([94]Fig. 2A). Additionally, there was no statistically significant
   variance in the Simpson index among the three groups ([95]Fig. 2A).
   These results indicated that a HFD led to a significant reduction in
   microbial richness and evenness, while the supplementation of matcha
   did not result in a significant improvement in microbial richness and
   evenness. In the analysis of Beta diversity, Principal Co-ordinate
   Analysis (PCoA) based on linear microbial community structure and
   Non-metric analysis of microbial structure Multidimensional Scales
   (NMDS) based on nonlinear microbial structure revealed a distinct
   dispersion trend across the three groups of mice, suggesting
   significant differences in gut microbiota structure among the groups
   ([96]Fig. 2B). The NMDS stress value of 0.08, below the threshold of
   0.1, could reflect the differences between samples. These results
   suggest that matcha supplementation can induce significant alterations
   in the gut microbiota structure of mice fed a HFD.

Fig. 2.

   [97]Fig. 2
   [98]Open in a new tab

   Matcha altered the gut microbiota structure of high-fat diet
   (HFD)-induced mice. (A) Alpha diversity analysis consisting ACE, Chao1,
   Shannon, and Simpson indices of gut microbiota among groups. (B)
   Principal Co-ordinate Analysis (PCoA) and Non-metric analysis of
   microbial structure Multidimensional Scales (NMDS) in Beta diversity
   analysis. (C)The Venn diagram shows the number of unique and shared
   OTUs and (D) the stack plot shows species composition and relative
   abundance at phylum level. (E) The bar chart shows the relative
   abundance of different genera. (F) Histogram and (G) cadogram of LDA
   values of biomarkers in the linear discriminant analysis effect size
   (LEfSe) analysis. In the histogram and cadogram, p, phylum; c, class;
   o, order; f, family; g, genus. In the cadogram, the circles radiating
   from inside to outside represent the taxonomic level from kingdom to
   species, with each circle at different taxonomic levels representing a
   species at that taxonomic level, and the size of the circle was
   proportional to the relative abundance. Data are expressed as mean ± SD
   (n = 6), *P < 0.05 and **P < 0.01.

3.2.2. Species composition analysis of gut microbiota

   The Venn diagram revealed that there were 280 identical OTUs present in
   all three groups of mice, with 490 OTUs unique to CK mice, 129 OTUs
   unique to MK mice, and 246 OTUs unique to M mice ([99]Fig. 2C). The
   composition of gut microbiota at the phylum level in the three groups
   of mice primarily consisted of Firmicutes, Bacteroida,
   Desulfobacterota, Verrucomicrobiota, Proteobacteria, and
   Actinobacteriota ([100]Fig. 2D). Among genera with a relative abundance
   exceeding 0.1, seven genera exhibited significant alterations compared
   to MK mice following matcha supplementation. The relative abundance of
   Alloprevotella, Ileibacterium, Rikenella, and Chlamydia in MK mice
   exhibited significant down-regulation compared to CK mice ([101]Fig.
   2E, P < 0.05), while Romboutsia showed a significant up-regulation
   ([102]Fig. 2E, P < 0.05). However, supplementation with matcha
   significantly reversed this situation (P < 0.05). Furthermore, matcha
   supplementation led to a significant increase in the relative abundance
   of Eubacterium_fissicatena_group and a significant decrease in the
   relative abundance of Eubacterium_brachy_group ([103]Fig. 2E,
   P < 0.05).

3.2.3. LEfSe analyze

   The LEfSe analysis was employed to identify biomarkers with LDA scores
   exceeding 4 within each group. At the genus level, the biomarkers
   identified in CK mice included Allobaculum, Candidatus_Saccharimonas,
   and Lachnospiraceae_NK4A136_group. Conversely, the biomarkers in MK
   mice were Coriobacteriaceae_UCG_002, Dubosiella, Blautia, and Dietzia,
   while M mice exhibited Akkermansia and Faecalibaculum as biomarkers
   ([104]Fig. 2F and G).

3.3. Effects of matcha on gut metabolites of mice

   In both positive and negative ion modes, the results of principal
   component analysis (PCA), partial least squares discriminant analysis
   (PLS-DA), and orthogonal partial least squares discriminant analysis
   (OPLS-DA) demonstrate a notable segregation trend among the three
   groups of mice. Additionally, all comparison models had a Q2Y value
   surpassing 0.5, indicating the reliability of the model predictions
   ([105]Supplement Figs. 2a–c). Metabolites with VIP >1 and P < 0.05 were
   identified as differential metabolites, resulting in the screening of a
   total of 2797 distinct metabolites. In comparison to CK mice, a total
   of 970 metabolites were found to be up-regulated and 982 down-regulated
   in MK mice, while 569 metabolites were up-regulated and 1262
   down-regulated in M mice ([106]Supplement Fig. 2d, P < 0.05).
   Furthermore, when compared to the MK mice, a total of 217 metabolites
   were up-regulated and 693 were down-regulated in M mice
   ([107]Supplement Fig. 2d, P < 0.05).

   The distribution of differential metabolites is demonstrated in the
   cluster heat map ([108]Fig. 3A). An investigation into the significance
   of these metabolites was carried out through KEGG pathway enrichment
   analysis. The results indicate that, compared to CK mice, there were
   significant alterations in nine and seven KEGG pathways in MK and M
   mice, respectively ([109]Fig. 3B, P < 0.05). Among these pathways,
   steroid hormone biosynthesis, eicosanoids, ABC transporters, ovarian
   steroidogenesis, and steroid biosynthesis were found to be
   significantly modified in both MK and M mice when compared to CK mice
   ([110]Fig. 3B, P < 0.05). In comparison to MK mice, M mice exhibited
   significant alterations in five KEGG pathways, namely Vitamin B6
   metabolism, caffeine metabolism, HIF-1 signaling pathway, cyanoamino
   acid metabolism, and glycine, serine, and threonine metabolism
   ([111]Fig. 3B, P < 0.05). Specifically, caffeine metabolism and HIF-1
   signaling pathway are hypothesized to play a significant role in the
   development of obesity. Meanwhile, compared to MK mice, caffeine
   metabolism and HIF-1 signaling pathway were significantly up-regulated
   in M mice ([112]Supplement Fig. 3, P <0.05). Additionally, compared to
   CK mice, the abundance of Vitamin C in the HIF-1 signaling pathway was
   significantly decreased in MK mice ([113]Fig. 3C, P < 0.05). In
   comparison to MK mice, the abundance of Vitamin C in the HIF-1
   signaling pathway, as well as theobromine and 1,3,7-trimethyluric acid
   in caffeine metabolism, were found to be significantly elevated in M
   mice ([114]Fig. 3C, P < 0.05). Furthermore, upon analyzing the
   physiological impacts of metabolites, it was observed that glutamic
   acid, pyroglutamic acid, and taurochenodeoxycholate, which are known to
   promote obesity ([115]Bagheri et al., 2019; [116]Cai et al., 2021).
   were significantly up-regulated in MK mice when compared to CK mice
   ([117]Fig. 3D, P < 0). Conversely, formononetin, a metabolite
   associated with the improvement of obesity, exhibited a significant
   downregulation ([118]Gautam et al., 2017). The supplementation of
   matcha significantly reversed the above change ([119]Fig. 3D,
   P < 0.05).

Fig. 3.

   [120]Fig. 3
   [121]Open in a new tab

   There was a significant difference in gut metabolites of mice after the
   treatment with matcha and high-fat diet (HFD). (A) Cluster analysis
   heat map. (B) Kyoto encyclopedia of genes and genomes (KEGG) pathway
   enrichment analysis: CK vs MK, CK vs M, and MK vs M. (C) Abundance of
   metabolites theobromine, 1,3,7-trimethyluric acid, Vitamin C, (D)
   glutamic acid, pyroglutamic acid, taurochenodeoxycholate, and
   formononetin. Data are expressed as mean ± SD (n = 6), *P < 0.05 and
   **P < 0.01.

3.4. Correlation analysis of gut microbiota, metabolites, and phenotypes

   To explore the relationship between gut metabolites and gut microbiota,
   the differential metabolites in the five KEGG pathways with significant
   differences between MK and M mice were correlated with genera having a
   relative abundance greater than 0.1 and significant differences between
   MK and M mice. The results indicate significant positive correlations
   between theobromine and Alloprevotella and Ileibacterium ([122]Fig. 4A,
   P < 0.05), as well as between 1,3,7-trimethyluric acid and
   Ileibacterium and Rikenella ([123]Fig. 4A, P < 0.05). Additionally,
   1,3,7-trimethyluric acid was found to have a significant negative
   correlation with Romboutsia ([124]Fig. 4A, P < 0.05). Furthermore,
   Vitamin C showed significant positive correlations with Alloprevotella,
   Ileibacterium, and Rikenella ([125]Fig. 4A, P < 0.05), while also
   exhibiting a significant negative correlation with Romboutsia
   ([126]Fig. 4A, P < 0.05). In addition, we performed correlations
   between all 2797 metabolites and microbial genera with relative
   abundance greater than 0.1. From this analysis, a total of 841
   metabolites were identified with a correlation coefficient r > 0.6 or
   r < −0.6, and had a statistically significant difference between MK and
   M mice. To aid in the interpretation of the results, these 841
   differential metabolites were categorized into groups including amino
   acids and their derivatives, bile acids, flavonoids ([127]Fig. 4B–D),
   and other metabolites ([128]Supplement Fig. 4). The results indicate
   significant negative correlations between glutamic acid, pyroglutamic
   acid, and taurochenodeoxycholate with Alloprevotella, Ileibacterium,
   and Rikenella ([129]Fig. 4B–C, P < 0.05), as well as positive
   correlations with Romboutsia ([130]Fig. 4B–C, P < 0.05). Conversely,
   formononetin had significant positive correlations with Alloprevotella,
   Ileibacterium, and Rikenella ([131]Fig. 4D, P < 0.05), and negative
   correlations with Romboutsia ([132]Fig. 4D, P < 0.05).

Fig. 4.

   [133]Fig. 4
   [134]Open in a new tab

   Heat map for correlation analysis between gut biomarkers and
   differential metabolites. (A) Heat map for metabolites in Kyoto
   encyclopedia of genes and genomes (KEGG) pathway, (B) amino acids and
   their derivatives, (C) bile acid and their derivatives and (D)
   flavonoids. The correlation coefficient r is shown in color. r > 0
   represents a positive correlation and is shown in red; r < 0 represents
   a negative correlation and is shown in blue. The darker the color, the
   stronger the correlation. *P < 0.05, and **P < 0.01. (For
   interpretation of the references to color in this figure legend, the