Examining gut microbiota and metabolites to clarify mechanisms of Dimocarpus longan Lour leaf components against type 2 diabetes

Abstract

BACKGROUND

Dimocarpus longan Lour leaf components (DLC) contain key active compounds such as quercetin, kaempferol, and quercitrin. They are effective for managing type 2 diabetes mellitus (T2DM), though the exact mechanism by which DLC acts remains unclear.

AIM

To investigate the material basis and mechanism underlying the therapeutic effect of DLC in T2DM.

METHODS

T2DM was triggered in rats using a high-sugar, high-fat diet alongside 35 mg/kg streptozotocin. The effect of DLC on the intestinal microbiota in T2DM rats was analyzed via 16S rDNA sequencing. Targeted metabolomics was conducted to evaluate the impact of DLC on the levels of nine short-chain fatty acids (SCFAs). Untargeted metabolomics examined DLC-induced alterations in fecal metabolites and associated metabolic pathways. Additionally, Spearman’s correlation analysis assessed gut microbiota and fecal metabolite relationships.

RESULTS

DLC significantly attenuated pathological weight loss, reduced fasting blood glucose levels, restored blood sugar homeostasis, and ameliorated dyslipidemia in T2DM rats. The 16S rDNA sequencing revealed that DLC enhanced microbial diversity and reversed intestinal dysbiosis. Targeted metabolomics indicated decreased acetic acid and propionic acid levels and increased butyric acid, isobutyric acid, and 2-methylbutyric acid levels after DLC treatment. Untargeted metabolomics revealed 57 metabolites with altered expression associated with amino acid, carbohydrate, purine, and biotin pathways. The Spearman analysis demonstrated significant links between specific gut microbiota taxa and fecal metabolites.

CONCLUSION

DLC may exert hypoglycemic effects by modulating intestinal flora genera, SCFA levels, and fecal metabolites.

Key Words: Dimocarpus longan Lour leaf components; Type 2 diabetes; 16S rDNA sequencing; Short-chain fatty acids; Metabolomics

Core Tip: This study investigated the anti-type 2 diabetes mechanism of Dimocarpus longan Lour leaf components (DLC). Results showed that DLC modulated intestinal flora, short-chain fatty acids, and fecal metabolites. It might exert hypoglycemic effects via the gut microbiota-metabolite-host axis, offering new insights for diabetes treatment.

INTRODUCTION

Diabetes mellitus (DM) is a metabolic dysfunction featuring persistent hyperglycemia stemming from hereditary or external influences. It involves impaired insulin secretion, which disrupts glucose and lipid homeostasis[1]. Currently, the global incidence of diabetes is rising sharply due to lifestyle changes. It is projected that the number of people with diabetes will exceed 700 million by 2045, with over 90% being cases of type 2 DM (T2DM)[2]. Consequently, the causes, prevention, and treatment of T2DM have garnered worldwide research focus, discovering effective medications is essential.

The leaves of Dimocarpus longan Lour (D. longan Lour) are a traditional Chinese medicine characterized by the Guangxi ethnicity. They possess a sweet, light, and flat taste and exhibit efficacy in dispelling heat, promoting diuresis, and detoxification. They are commonly utilized as a unified traditional Chinese medicine remedy for diabetes. Previous studies have shown that D. longan Lour leaves can significantly reduce the blood glucose level of hyperglycemic model mice[3]. Past research has indicated that the principal chemical constituents responsible for the hypoglycemic activity of D. longan Lour leaves are flavonoids[4]. The UHPLC-Q-Orbitrap MS method was used to examine metabolites from the ethyl acetate extract of D. longan Lour leaves in rats. The research results show that flavonoid components such as quercetin, kaempferol, quercitrin, and so on are likely to be the key pharmacological components of D. longan Lour leaves against T2DM[5]. Currently, the compatibility of components in traditional Chinese medicine has gradually become an important development direction of the drug compatibility mode. When the active components are combined in a particular proportion, the therapeutic effect of the medicine can be maximized. This study utilized the Central Composite Design-Response Surface Methodology to refine the experimental setup and explore the hypoglycemic properties of the flavonoid monomer components found in D. longan Lour leaves. In vitro experiments demonstrated that quercetin, kaempferol, and quercitrin markedly enhanced glucose uptake in insulin-resistant HepG2 cells, and the hypoglycemic effect was closely related to the ratio of these three components. The experimental findings established a 2:3:9 ratio as optimal for the three components, which created D. longan Lour leaf components (DLC)[6]. Building on the research team’s foundation, the hypoglycemic potential of DLC show significant promise. However, the mechanism of its action remains unclear.

Growing evidence suggests that the balance of the intestinal microenvironment is closely linked to the onset of T2DM[7]. Patients with T2DM display gut microbiota imbalance, marked by reduced microbial diversity and elevated harmful bacteria. For instance, sulfate-reducing and oxidative bacteria contribute to this imbalance[8], making the gut microbiota a potential therapeutic target for T2DM. Additionally, bacterial metabolites, including short-chain fatty acids (SCFAs), influence gastrointestinal hormone release, glucose and lipid processing, and insulin responsiveness, contributing to T2DM development[9]. As co-metabolites of bacteria and the host, fecal endogenous metabolites reflect bacterial status and mediate host-microbiota interactions[10]. Thus, DLC potentially mitigates T2DM via alterations in intestinal flora, SCFA synthesis, and internal metabolites. However, this hypothesis requires further verification. From gut microbiota and metabolomics perspectives, specific DLC monomers, such as quercetin, have shown therapeutic potential. Quercetin, for instance, lowers glycemia, mitigates insulin resistance (IR), restores gut integrity, modulates intestinal flora, and modifies metabolites in db/db murine models[11]. From the holistic perspective of traditional Chinese medicine, DLC can act on multiple targets synergistically. However, DLC's effect on gut microbiota among those with T2DM remains unexamined. This prompted us to investigate how DLC modulates the gut microbiome to alleviate T2DM, aiming to advance the rational development and utilization of D. longan Lour leaves. The research approach is shown in Figure 1A.

Figure 1 Influence of Dimocarpus longan Lour leaf components on type 2 diabetes mellitus rats. A: Experimental protocol for the treatment of type 2 diabetes mellitus (T2DM) rats by Dimocarpus longan Lour leaf components (DLC); B: DLC treatment mitigated weight loss in T2DM rats; C: DLC therapy reduced fasting blood glucose levels in T2DM rats; D and E: The area under the curve of oral glucose tolerance test was lowered in T2DM rats following DLC treatment; F-I: Alterations in blood lipids post-DLC therapy. Results were expressed as mean ± SD (n = 6). ^aP < 0.05 vs the control (Con) group; ^bP < 0.01 vs the Con group; ^dP < 0.01 vs the model group. HDL-C: High-density lipoprotein cholesterol; Con: Control group; Mod: The model group; Met: The metformin group; HDLC: The high-dose group; MDLC: The medium-dose group; LDLC: The low-dose group; AUC: Area under the curve; OGTT: Oral glucose tolerance test. Figure 1A created with BioRender.com (Supplementary material).

MATERIALS AND METHODS

Materials

The high-sugar and high-fat diet (HSHFD, XC200608002) was purchased from the Beijing Boai Gang Trade Centre (Beijing, China). Streptozotocin (STZ, S0130) was sourced from Sigma-Aldrich (MO, United States). Total cholesterol (TC, 20230506), triglyceride (TG, 20230506), low-density lipoprotein cholesterol (LDL-C, 20230507), and high-density lipoprotein cholesterol (HDL-C, 20230507) assay kits were obtained from Nanjing Jiancheng Bioengineering Institute (Jiangsu Province, China). Metformin hydrochloride tablets (2208020) were acquired from Beijing Jingfeng Pharmaceutical Group Company Limited (Beijing, China). Quercetin (RP200910), kaempferol (RP200312), and quercitrin (RP200602) were provided by Chengdu Maidesheng Technology Co., Ltd. (Sichuan Province, China). All remaining chemicals and reagents met analytical quality.

Preparation of liquids

Quercetin, kaempferol, and quercitrin, identified as the hypoglycemic active constituents of D. longan Lour leaves, were combined in a ratio of 2:3:9 to yield DLC.

Animals and experimental design

Forty specific pathogen free (SPF) male Sprague-Dawley (SD) rats (SCXK 2021-0002), weighing 200 ± 20 g, were sourced from Hunan Slike Jing da Laboratory Animal Co., Ltd. The rats were kept in a regulated environment where the temperature was stabilized at 23 ± 1 °C, the relative humidity fluctuated between 40% and 60%, and a 12-hour light and 12-hour dark cycle was implemented. The rats were provided unrestricted food and water. All animal experiments occurred at the Animal Experiment Center of the Guangxi University of Chinese Medicine (Nanning, China). Our study adhered strictly to the ARRIVE guidelines. Animal studies received approval from the Guangxi University of Traditional Chinese Medicine Ethics Committee (Approval No. 20230830-157). Experiments adhered to animal welfare standards, prioritizing minimal distress.

Following a week of acclimatization feeding, the 40 rats were randomly assigned to a control (Con) group (n = 6) and an HSHFD group (n = 34). The Con group received a standard diet, whereas the HSHFD group was given the HSHFD for 4 weeks. After 4 weeks, the 34 rats in the HSHFD group were fasted overnight (12 hours) with unrestricted water access followed by STZ (35 mg/kg, dissolved in 0.10 mol/L citrate buffer, pH 4.5) intraperitoneal injection to establish the T2DM model. The Con group rats received a comparable volume of citrate buffer alone. Following 72 hours of induction, random glucose readings were taken. Values above 16.7 mmol/L confirmed successful T2DM modeling[12]. Excluding rats not meeting the model criteria (n = 4), the remaining T2DM rats (n = 30) were subdivided into five groups: The model (Mod) group, the metformin (Met) group, the high-dose (HDLC) group, the medium-dose (MDLC) group, and the low-dose (LDLC) group, each consisting of six rats. Throughout the experiment, the HDLC, MDLC, and LDLC groups were administered DLC at doses of 0.28 g/kg, 0.14 g/kg, and 0.075 g/kg, respectively, via gavage; the Met group was administered 0.1 g/kg metformin orally. Saline was gavaged once daily for 28 days in the Con and Mod groups. The rats were provided unrestricted food access, and their weight was regularly tracked. Following the trial, rat fecal matter was aseptically collected in Eppendorf tubes and stored at -80 °C for further analysis of gut microbiome, SCFA, and metabolomics.

Fasting blood glucose measurement

Following 12 hours of fasting, tail-tip blood samples were collected, and fasting blood glucose (FBG) levels were determined using a glucose meter.

Oral glucose tolerance test

After 4 weeks of DLC treatment, all rats underwent a 12-hour fast. Blood glucose levels were quantified by testing strips and recorded as the baseline value at time point 0. Following this, each rat was administered a 40% glucose solution at a 5 mL/kg dose via gavage. Blood glucose was assessed at 0, 30, 60, 90, and 120 minutes after glucose consumption. The acquired data were then employed to generate blood glucose curves, thereby enabling the calculation of the area under the curve (AUC) for the oral glucose tolerance test (OGTT).

Serum analysis

After 4 weeks of DLC treatment, all rats underwent a 12-hour fast. Then, the rats were sedated via intraperitoneal pentobarbital sodium injection at 50 mg/kg[13]. Blood was collected from the abdominal aorta, centrifuged at 3000 rpm for 15 minutes to separate serum, and analyzed for TC, TG, HDL-C, and LDL-C levels following kit protocols.

Fecal 16S rDNA sequencing and data analysis

Isolate DNA from fecal metabolite samples following the instructions in the DNA extraction kit. Perform PCR to amplify the V3-V4 segment of the 16S rDNA gene using primers 515F (5′-GTGCCAGCMGCCGCGGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The purified amplicons undergo paired-end library sequencing using the Illumina NovaSeq platform. Denoise, splice, and remove chimeras from the original sequence files of the raw sequences on the QIME2 platform to acquire the amplicon sequence variant (ASV) feature sequence table. Utilize the QIME2 platform for tasks such as species annotation, analysis of community composition, and evaluations of both alpha and beta diversity to assess the shifts in microorganisms present in the intestinal contents.

Quantification of SCFAs

Samples (100 mg) were homogenized with 400 μL of methanol (80%) and centrifuged to eliminate the protein content. The resulting supernatant was reacted with 150 μL derivatization reagent and incubated at 40 °C for 40 minutes to complete the modification. Then, the supernatant (190 μL) was combined with 10 μL of the internal standard solution. This mixture was subsequently introduced into the LC-MS system for examination. Regarding the chromatographic conditions, the analysis was conducted using a Waters ACQUITY UPLC BEH C18 column (dimensions: 2.1 mm × 100 mm; particle size: 1.7 μm) maintained at 40 °C. The mobile phase consisted of solvent A (10 mmol/L ammonium acetate in water) and solvent B (a 1:1 blend of acetonitrile and isopropanol), flowing at 0.30 mL/minute. The mass spectrometer was configured for negative multiple reaction monitoring using these settings: A -4500 V ion spray voltage, a sheath gas pressure of 35 psi, an ion source temperature cranked up to 550 °C, an auxiliary gas pressure of 50 psi, and a collision gas pressure set at 55 psi.

Metabolomics profiling of fecal samples

Samples (100 mg) were individually ground with liquid nitrogen, homogenized, and vortexed in prechilled 80% methanol. The samples were promptly cooled on ice for a brief five-minute interval before undergoing centrifugation at 15000 g, maintained at a chilly 4 °C, for 20 minutes. Subsequently, a portion of the resulting supernatants was blended with LC-MS grade water to achieve a methanol concentration of 53%. The freshly prepared solutions were transferred to new Eppendorf tubes and re-centrifuged using the same settings. Eventually, the supernatant was readied for LC-MS examination. The sample was introduced into a Hypesil Gold column (100 mm × 2.1 mm, 1.9 μm) with a 12-minute linear gradient at a steady flow rate of 0.2 mL/minute. In positive polarity mode, the mobile phase comprised eluent A, a 0.1% formic acid solution in water, and eluent B, pure methanol. For the negative polarity mode, eluent A switched to a 5 mmol/L ammonium acetate solution adjusted to pH 9.0, while eluent B stayed as methanol. The solvent gradient was programmed as follows: Starting at 2% B for the first 1.5 minutes, ramping up to 85% B over the next 3 minutes, then climbing to 100% B by the 10-minute mark, before swiftly returning to 2% B at 10.1 minutes and holding there until the 12-minute endpoint. The Q Exactive™ HF mass spectrometer was configured to run in both positive and negative polarity modes, utilizing a spray voltage set at 3.5 kV. The capillary temperature was maintained at 320 °C, while the sheath gas flowed at 35 psi and the auxiliary gas at 10 L/minute. The S-lens RF level was adjusted to 60, and the auxiliary gas heater was kept at a steady 350 °C.

Correlation analysis

Spearman’s correlation evaluated the link between gut microbiota and T2DM biomarkers. In this research, Spearman's correlation coefficients indicated statistically significant positive correlations when r exceeded 0.5 and significant negative correlations when r fell below -0.5.

Statistical analysis

The data analysis was performed using SPSS 22.0 and GraphPad Prism 9, with results expressed as mean ± SD. Prior to running parametric tests, the Shapiro-Wilk test was employed to check for normality in the data. For normally distributed datasets, one-way ANOVA followed by Tukey’s post hoc test was used to compare group differences. On the other hand, non-normally distributed data were analyzed using the Kruskal-Wallis rank-sum test, complemented by Dunn’s post hoc analysis. To account for multiple comparisons, the Benjamini-Hochberg false discovery rate method was applied, with statistical significance set at P < 0.05. Differences in metabolite levels between groups were evaluated using Student’s t-test and fold change calculations. Additionally, Spearman correlation analysis was conducted to explore potential relationships between gut microbiota composition and specific metabolites.

RESULTS

Impact of DLC on weight in T2DM rats

As shown in Figure 1B, following STZ injection, rats exhibited typical symptoms characteristic of diabetes, presenting with weight loss. Following 4 weeks of intervention, the Mod group showed a notable decrease in weight relative to the Con group (P < 0.01). In addition, the Met group showed a marked elevation in weight (P < 0.01) in contrast to the Mod group, while the HDLC, MDLC, and LDLC groups also demonstrated noteworthy weight increases (P < 0.01) relative to the Mod group. These findings point to the idea that DLC has the potential to alleviate weight loss symptoms in diabetic rats.

Effects of DLC on FBG levels in T2DM rats

Before DLC administration, in comparison with the Con group, the FBG levels in all other groups were markedly elevated (P < 0.01), which signified the successful establishment of the T2DM rat model. Following 4 weeks of DLC treatment, FBG levels in the HDLC, MDLC, and LDLC groups significantly decreased relative to the Mod group (P < 0.01). This indicates that DLC can improve abnormal blood glucose levels in T2DM rats. These findings are shown in Figure 1C.

Effects of DLC on OGTT in T2DM rats

The Mod group exhibited consistently elevated basal blood glucose levels than the Con group at all time points (P < 0.01). In contrast, the Met group, HDLC group, MDLC group, and LDLC group, the blood glucose levels gradually decreased after 30 minutes. At 120 minutes, the blood glucose levels in these four groups were all significantly lower than those in the Mod group (P < 0.01 or P < 0.05). The AUC of the OGTT showed a notable rise in the Mod group relative to the Con group (P < 0.01). In contrast, the AUC of OGTT exhibited a notable decline (P < 0.01) in the Met, HDLC, MDLC, and LDLC groups relative to the Mod group. This indicates that DLC maintains glucose tolerance. These findings are shown in Figure 1D and E.

Influence of DLC on blood lipid levels in T2DM rats

In comparison to the Con group, rats in the Mod group exhibited significantly increased serum levels of TG, TC, and LDL-C (P < 0.01). In contrast, HDL-C levels were conspicuously diminished (P < 0.01). Following the treatment, the Met, HDLC, MDLC, and LDLC groups showed a marked decrease in serum levels of TC, TG, and LDL-C (P < 0.01) compared to the Mod group. Furthermore, the Met and HDLC groups experienced a significant boost in HDL-C levels (P < 0.01). These results indicate that DLC ameliorates disturbances in blood lipid metabolism and mitigates T2DM, with the most significant effects observed at higher doses (Figure 1F-I). These investigations indicated that the HDLC group significantly altered body weight, FBG levels, the AUC of OGTT, and lipid profile. Thus, the HDLC group was chosen for further studies on gut microbiota, SCFAs, and metabolomics.

DLC modifies gut microbiota in T2DM rats

α and β diversity analysis: 16S rDNA sequencing was used to analyze changes in the gut microbiome of T2DM rats after HDLC administration. The Venn diagram (Figure 2A) delineates shared OTUs among the groups through overlapping regions and unique OTUs specific to each group through non-overlapping areas. The analysis showed that the five groups had 209 shared OTUs, with the Mod group displaying the most unique OTUs. Furthermore, the Chao1 and Observed-ASV indices in the Mod group were lower than in the Con group, but the difference was not statistically significant (P > 0.05). However, this indicates that the microbial richness in T2DM rats has decreased. Notably, when compared to the Mod group, the HDLC group demonstrated a notable rise in the Shannon index (P = 0.033), while the Chao1, Observed-ASV, and Simpson indices also trended upward, though these increases did not reach statistical significance (P > 0.05). This suggests that the DLC treatment effectively enhanced microbial richness and diversity (Figure 2B-E). Principal component analysis (PCA) based on Bray-Curtis distances revealed significant differences in gut microbiota composition between the Con and Mod groups (Figure 2F), indicating a significant shift in gut microbiota structure between T2DM and normal rats. Furthermore, DLC treatment effectively rectified the alterations caused by T2DM, making the changes in the HDLC group more analogous to those in the Con group. A similar pattern was noticeable in the nonparametric multidimensional scaling (NMDS) analysis based on Bray-Curtis distances (Figure 2G). These results emphasize the substantial influence of DLC treatment on gut microbiota composition.

Figure 2 Impact of Dimocarpus longan Lour leaf components on gut microbial diversity in type 2 diabetes mellitus rats. A: Venn diagram; B: Observed-amplicon sequence variant index; C: Chao1 index; D: Shannon index; E: Simpson index; F: Bray Curtis-based principal component analysis; G: Bray Curtis-based nonparametric multidimensional scaling. Results were expressed as mean ± SD (n = 6). All variations were assessed via one-way ANOVA with Tukey-Kramer post hoc analysis; multiple comparisons were adjusted using the Benjamini–Hochberg false discovery rate method. Con: Control group; Mod: The model group; Met: The metformin group; HDLC: The high-dose group; ASV: Amplicon sequence variant.

Alterations in the microbial composition at the Phylum and genus levels: At the phylum level (Figure 3A-D), the makeup and arrangement of gut microbiota are vital for preserving intestinal balance, and any alterations in these factors are intricately linked to the overall function of the intestines. Research indicates p__Firmicutes and p__Bacteroidetes predominate in rat gut microbiota. In contrast to the Con group, the intestinal bacteria in the Mod group displayed altered composition, specifically a decreased proportion of p__Firmicutes (P < 0.01), while the proportional presence of p__Bacteroidetes rose notably (P < 0.01). Additionally, the proportion of p__Actinobacteria also increased. Specifically, a decline in the proportion of p__Firmicutes could result in a decrease in SCFAs. As a result, this process can compromise the integrity of the intestinal lining, weaken the gut barrier’s defenses, and allow harmful endotoxins to seep into the bloodstream, ultimately setting the stage for persistent inflammation[14]. Moreover, the heightened relative presence of p__Bacteroidetes is linked to irregular glucose metabolism and could influence T2DM by modifying the gut microbiota's functions[15]. An increase in p__Actinobacteria may overactivate the immune system, releasing many inflammatory factors and exacerbating the inflammation in the body. This can impair pancreatic islet β cell function, disrupt insulin secretion and activity, and exacerbate T2DM progression[16]. Following DLC intervention, the HDLC group rats exhibited p__Firmicutes, p__Bacteroidetes, and p__Actinobacteria levels more aligned with the Con group. These findings indicate that DLC could enhance T2DM-linked metabolic issues by altering gut microbiota composition and stimulating SCFA-producing bacterial proliferation.

Figure 3 Alterations in the microbial composition at the phylum and genus levels. A: The proportion of intestinal microbiota at the phylum level; B: The proportion of Firmicutes; C: The proportion of Bacteroidetes; D: The proportion of Actinobacteria; E: The proportion of gut microbiota at the genus levels; F: Community heatmap analysis on genus level. Results were expressed as mean ± SD (n = 6). ^bP < 0.01 vs the control group; ^cP < 0.05 vs the model group. Con: Control group; Mod: The model group; Met: The metformin group; HDLC: The high-dose group.

At the genus level (Figure 3E and F), g__Ligilactobacillus is the most abundant genus in the rat gut. In the Mod group, the proportion of g__Ligilactobacillus significantly decreased compared to the Con group (P < 0.01), whereas the levels of g__Prevotella_9, g__Alloprevotella, and g__Anaerovibrio in the Mod group showed a significant rise (P < 0.01). In contrast to the Mod group, the HDLC group exhibited a marked reduction in the relative prevalence of g__Prevotella_9, g__Alloprevotella, and g__Anaerovibrio (P < 0.01), while g__Ligilactobacillus and g__Roseburia levels rose in the HDLC group. The proportion of g__Ligilactobacillus and g__Streptococcus in the Met group increased significantly (P < 0.01). The proportion of g__Prevotella_9, g__Alloprevotella, and g__Anaerovibrio in the Met group decreased significantly (P < 0.01). In summary, at the genus level, DLC and metformin intervention can significantly improve intestinal flora changes caused by HSHFD feeding.

Functional predictive analytics: As illustrated in Figure 4, the Tax4Fun functional prediction analysis highlights the KEGG pathway, which shows significant enrichment in various metabolic processes. These include the metabolism of carbohydrates, amino acids, energy, and nucleotides, underscoring their dominant roles within the pathway.

Figure 4 Functional predictive analytics.

Effects of DLC on SCFAs in T2DM rats

In contrast to the Con group, the Mod group exhibited substantial increases in acetic acid and propionic acid levels by 48.9% and 197.2%, respectively (P < 0.01). It is notable that as a systemic energy metabolic substrate, the excessive accumulation of acetic acid may exacerbate the host's lipid metabolism disorder, and the increase in the level of propionic acid may be due to abnormal intestinal glucose metabolism. At the same time, the levels of seven SCFAs, including butyric acid, 2-methylbutyric acid, and isobutyric acid, saw a marked reduction of 83.4% to 100% (P < 0.01 or P < 0.05). The reduction in butyric acid and other SCFA levels reflects compromised intestinal barrier integrity. After the DLC intervention, the levels of acetic acid decreased by 17.1% (P > 0.05), the levels of propionic acid significantly decreased by 94.1%, and the levels of butyric acid significantly increased by 85.5% (P < 0.01). Meanwhile, other branched-chain SCFAs (2-methylbutyric acid, isobutyric acid, and isovaleric acid) were synchronously restored by 93.6%-100% (P < 0.01 or P < 0.05). DLC improves the metabolic disorder of the intestinal flora by regulating the levels of acetic acid and propionic acid and significantly increases the content of butyric acid (the core energy source of intestinal epithelial cells), thus improving tight junction protein expression and restoring intestinal barrier integrity[17]. At the same time, the synchronous restoration of branched-chain SCFAs such as 2-methylbutyric acid, isobutyric acid, and valeric acid further suggests that DLC could synergistically modulate gut microbiota's breakdown of branched-chain amino acids. The findings are illustrated in Figure 5.

Figure 5 Effects of Dimocarpus longan Lour leaf components on short-chain fatty acids in type 2 diabetes mellitus rats. A: Acetic acid; B: Propionic acid; C: Butyric acid; D: Isobutyric acid; E: 2-Methylbutyrate acid; F: Valeric acid; G: Isovaleric acid; H: Hexanoic acid; I: 4-Methylvaleric acid. Results were expressed as mean ± SD (n = 6). ^aP < 0.05 vs the control (Con) group; ^bP < 0.01 vs the Con group; ^cP < 0.05 vs the model (Mod) group; ^dP < 0.01 vs the Mod group. Con: Control group; Mod: The model group; Met: The metformin group; HDLC: The high-dose group.

Impact of DLC on serum metabolites in rats with T2DM

Multivariate statistical analysis: The PCA analysis monitored intra-group similarity and inter-group variability of the samples as a whole. Notable differences were observed among the Con, Mod, and HDLC groups in both positive and negative ion modes (Figure 6A and B). The fecal metabolome of each group was assessed using the orthogonal partial least squares - discriminant analysis (OPLS-DA) model to identify potential biomarkers. In both ion modes, the Con and HDLC groups were clearly distinct from the Mod group. OPLS-DA models showed strong fit (R²Y > 0.9) and predictive ability (Q² > 0.5). To verify the reliability of the OPLS-DA models, a permutation test was conducted 200 times. The results demonstrated that the slopes of R²Y and Q² for all OPLS-DA models were positive. In the permutation test plots, the R²Y and Q² values on the left side of each OPLS-DA model were lower than the original values on the right side, and the intercept of the regression line for Q² was less than 0.05, indicating the OPLS-DA models have robust predictive power without overfitting (Figure 6C-J).

Figure 6 Multivariate statistical analysis (n = 6). A: Principal component analysis (PCA) score plot (positive ion mode); B: PCA score plot (negative ion mode); C and D: Orthogonal partial least squares - discriminant analysis (OPLS-DA) score plot (positive ion mode); E and F: OPLS-DA score plot (negative ion mode); G and H: OPLS-DA model validation in positive ion mode via permutation test (n = 200); I and J: OPLS-DA model validation in negative ion mode via permutation test (n = 200). R²Y (model interpretability) and Q²Y (predictive power) are critical metrics for OPLS-DA. A robust model is confirmed when R²Y > Q²Y. Con: Control group; Mod: The model group; Met: The metformin group; HDLC: The high-dose group.

Potential marker analysis: Through the OPLS-DA model, VIP values of characteristic mass spectral signals were derived. Signals with VIP > 1 were selected, followed by t-test to pick those with P < 0.05, and finally, signals with FC > 2 were chosen by FC value. The findings revealed that in positive ion mode, the Con and Mod groups of rats exhibited 90 mass spectral signals that were ramped up and 120 that were dialed down. Switching gears to negative ion mode, we observed 59 signals boosted and 94 signals suppressed. To explore the impact of DLC on fecal metabolism in T2DM rats, the HDLC group was contrasted with the Mod group, and differential metabolites were screened using the same approach. The results demonstrated that under positive ion conditions, the Mod and HDLC groups exhibited 123 mass spectral signals with increased intensity and 107 with decreased intensity. Conversely, in negative ion mode, 57 signals showed up-regulation, while 81 were down-regulated (Figure 7). The focus of this section was on certain specific metabolites that exhibited significant alterations as differential metabolites between the Con and Mod groups and underwent further substantial changes upon the therapeutic application of the DLC drug. A total of 57 differential metabolites possessing these characteristics was detected and considered as potential biomarkers. These results are presented in Table 1.

Figure 7 Potential marker analysis. A volcano diagram illustrating the distinct metabolite compositions for control group vs model group and model group vs the high-dose group (The X-axis represents the diversity of metabolite variations across groups, while the Y-axis denotes the statistical significance of these variations). Con: Control group; Mod: The model group; Met: The metformin group; HDLC: The high-dose group.

Table 1 Differential metabolites.

No.	Metabolites	tR/minute	m/z	Formula	Mode	HMDB ID	Trend
							Con/Mod	Mod/DLC
1	Eriodictyol	5.652	269.04530	C15H12O6	ESI-	HMDB0005810	↓	↑
2	N-Acetyl-D-galactosamine 4-sulfate	1.307	300.03916	C8H15NO9S	ESI-	HMDB0000781	↑	↑
3	α-ketoglutaric acid	1.481	145.01309	C5H6O5	ESI-	HMDB0000208	↑	↓
4	Palmitic acid	10.254	255.23257	C16H32O2	ESI-	HMDB0000220	↓	↑
5	2,3-Dihydroxybenzoic acid	4.998	153.01833	C7H6O4	ESI-	HMDB0000397	↓	↑
6	L-Ascorbic acid 2-sulfate	1.512	254.98142	C6H8O9S	ESI-	HMDB0060649	↓	↑
7	2-Methylglutaric acid	5.244	145.04955	C6H10O4	ESI-	HMDB0000422	↑	↓
8	Ascorbic acid	2.571	175.02375	C6H8O6	ESI-	HMDB0000044	↓	↑
9	4-Methylvaleric Acid	5.819	231.15956	C6H12O2	ESI-	HMDB0000689	↑	↓
10	Taurolithocholic acid 3-sulfate	5.789	562.24355	C26H45NO8S2	ESI-	HMDB0002580	↓	↑
11	Caffeic acid	5.337	179.03762	C9H8O4	ESI-	HMDB0001964	↓	↑
12	Citramalic acid	4.201	147.03161	C5H8O5	ESI-	HMDB0000426	↓	↑
13	7-Ketolithocholic acid	6.960	389.26952	C24H38O4	ESI-	HMDB0000467	↑	↓
14	Anacardic acid	8.440	347.25888	C22H36O3	ESI-	HMDB0033896	↑	↓
15	Phenylpyruvic acid	5.521	163.03907	C9H8O3	ESI-	HMDB0000205	↑	↓
16	Gamma-Caprolactone	5.150	273.13431	C6H10O2	ESI-	HMDB0003843	↑	↓
17	Syringic acid	3.826	197.04482	C9H10O5	ESI-	HMDB0002085	↓	↑
18	Asp-Phe	5.159	279.09876	C13H16N2O5	ESI-	HMDB0000706	↑	↓
19	Palmitoylcarnitine	8.360	398.32717	C23H45NO4	ESI-	HMDB0240774	↓	↑
20	L-Kynurenine	3.964	207.07683	C10H12N2O3	ESI-	HMDB0000684	↑	↓
21	2,3,4-Trihydroxybenzoic acid	4.767	169.01344	C7H6O5	ESI-	HMDB0059964	↓	↑
22	7-Methylxanthine	2.950	165.04090	C6H6N4O2	ESI-	HMDB0001991	↑	↓
23	N-Acetyl-L-histidine	1.349	196.07196	C8H11N3O3	ESI-	HMDB0032055	↑	↓
24	D-Glucosamine 6-phosphate	1.287	258.03813	C6H14NO8P	ESI-	HMDB0001254	↓	↑
25	Allantoin	1.297	157.03556	C4H6N4O3	ESI-	HMDB0000462	↓	↑
26	LPC 20: 3	8.96	590.34664	C28H52NO7P	ESI-	-	↓	↑
27	ADP-ribose	1.390	560.07848	C15H21N5O13P2	ESI-	HMDB0249529	↓	↑
28	Creatine	1.321	132.07689	C4H9N3O2	ESI+	HMDB0000064	↓	↑
29	N6,N6,N6-Trimethyl-L-lysine	1.180	189.16000	C9H20N2O2	ESI+	HMDB0001325	↓	↑
30	Propionylcarnitine	3.040	218.13907	C10H19NO4	ESI+	HMDB0000824	↓	↑
31	Biotin	5.387	245.09590	C10H16N2O3S	ESI+	HMDB0000030	↑	↓
32	Argininosuccinic acid	1.283	291.12971	C10H18N4O6	ESI+	HMDB0000052	↓	↑
33	Glycitein	5.804	285.07588	C16H12O5	ESI+	HMDB0005781	↓	↑
34	L-Saccharopine	1.248	277.13934	C11H20N2O6	ESI+	HMDB0000279	↓	↑
35	Histidine	1.376	139.05028	C6H9N3O2	ESI+	HMDB0000177	↑	↓
36	3-(3,4-dihydroxyphenyl) propanoic acid	5.224	165.05499	C9H10O4	ESI+	HMDB0000423	↓	↑
37	Glu-Thr	1.371	114.06653	C9H16N2O6	ESI+	HMDB0028829	↓	↑
38	N-(5-Aminopentyl) acetamide	1.743	145.13362	C7H16N2O	ESI+	HMDB0002284	↓	↑
39	Mycophenolic acid	1.242	321.12973	C17H20O6	ESI+	HMDB0015159	↓	↑
40	3-Methoxytyramine	5.595	150.09125	C9H13NO2	ESI+	HMDB0000022	↑	↓
41	Ornithine	1.140	116.07090	C5H12N2O2	ESI+	HMDB0000214	↑	↓
42	Muscone	9.526	239.23704	C16H30O	ESI+	HMDB0034181	↓	↑
43	Ethyl oleate	9.587	311.29466	C20H38O2	ESI+	HMDB0034451	↓	↑
44	4-Guanidinobutanoic acid	5.254	146.09272	C5H11N3O2	ESI+	HMDB0003464	↑	↓
45	Arachidonic acid methyl ester	6.421	319.26285	C21H34O2	ESI+	HMDB0062594	↑	↓
46	Citrulline	1.890	176.10324	C6H13N3O3	ESI+	HMDB0250742	↑	↓
47	Xanthurenic acid	5.081	206.04522	C10H7NO4	ESI+	HMDB0000881	↑	↓
48	D-Alanyl-D-alanine	5.737	161.09236	C6H12N2O3	ESI+	HMDB0003459	↓	↑
49	Leu-Pro	3.991	211.14435	C11H20N2O3	ESI+	HMDB0011175	↓	↑
50	gamma-Glutamyltyrosine	4.943	311.12415	C14H18N2O6	ESI+	HMDB0011741	↑	↓
51	Xanthine	2.026	153.04077	C5H4N4O2	ESI+	HMDB0000292	↑	↓
52	dAMP	1.990	332.07433	C10H14N5O6P	ESI+	HMDB0000905	↓	↑
53	Uracil	1.681	113.03489	C4H4N2O2	ESI+	HMDB0000300	↑	↓
54	Urocanic acid	1.378	139.05026	C6H6N2O2	ESI+	HMDB0000301	↑	↓
55	22(S)-Hydroxycholesterol	9.474	403.3570	C27H46O2	ESI+	-	↓	↑
56	PC O-20:3	8.945	546.35561	C28H52NO7P	ESI+	-	↓	↑
57	PC O-18:1	9.181	522.35554	C26H52NO7P	ESI+	-	↓	↑

DLC: Dimocarpus longan Lour leaf components; Con: Control group; Mod: The model group.

Metabolic marker-related pathway analysis

To pinpoint affected pathways, differentially abundant metabolites were analyzed via MetaboAnalyst 6.0 for enrichment analysis. As shown in Figure 8, the intensity of the circle's hue aligns with the -log (P) value's scale, highlighting the pathway's significance. Moreover, the diameter of each circle serves as a visual cue for the extent of its impact. These findings suggest that these metabolites are primarily connected to the biosynthesis of Arginine, alongside the metabolism of arginine and proline, histidine, alanine, aspartic acid, and glutamate. They also relate to the degradation of lysine, purine metabolism, phenylalanine metabolism, biotin metabolism, and the tricarboxylic acid (TCA) cycle (citrate cycle).

Figure 8 Metabolic pathway analysis of type 2 diabetes mellitus-linked biomarker candidates. a: Arginine biosynthesis; b: Arginine and proline metabolism; c: Histidine metabolism; d: Alanine, aspartic acid, and glutamate metabolism; e: Lysine degradation; f: Purine metabolism; g: Phenylalanine metabolism; h: Biotin metabolism; i: Tricarboxylic acid cycle (citrate cycle).

Correlation analysis of intestinal flora with untargeted metabolomics

Spearman correlation analysis was conducted to explore genus-level gut microbiota relationships in the Con, Mod, and HDLC groups. Spearman's rank correlation coefficient serves as a non-parametric tool to assess the statistical interdependence between two variables, gauging whether their association follows a monotonic trend. In our research, we leveraged this approach to investigate the intensity and nature of connections between the relative proportions of gut microbiota genera and the concentrations of fecal metabolites. As shown in Figure 9, Ligilactobacillus, Prevotella_9, Alloprevotella, Roseburia, and Anaerovibrio exhibited correlations with most of the metabolites.

Figure 9 Correlation analysis of the intestinal flora with untargeted metabolomics. The vertical axis illustrates the varying abundance of gut microbiota. The color coding within the grids reflects the outcomes of the Spearman correlation analysis. Red grids signify positive correlations, where the correlation value exceeds 0.1, whereas blue grids represent negative correlations with values falling below -0.1. The color gradient in the heatmap represents these correlation values, with more intense shades of red or blue indicating stronger correlations.

DISCUSSION

T2DM is a chronic disease that is currently reaching epidemic proportions worldwide. In this research, a combination of HSHFD and STZ injection was used to induce a rat model of T2DM. The HDLC markedly decreased weight loss, fasting glucose levels, and serum lipid abnormalities in rats relative to the Mod group. The results indicate that DLC exerts a therapeutic effect on T2DM, especially in HDLC. Moreover, metformin was chosen as the positive control for T2DM in our study, as it is the primary clinical treatment for this condition[18]. DM is associated with an abnormal lipid metabolism. TG, TC, and LDL-C levels are commonly elevated, along with lowered HDL-C levels in the blood[19]. This study showed that DLC lowers TC, TG, and LDL-C while raising HDL-C in diabetic rat serum. The OGTT results indicated glucose intolerance in diabetic rats, whereas DLC supported blood sugar regulation.

The 16S rDNA analysis examined DLC's influence on intestinal bacteria in T2DM rats. The complexity and variety of the microbial community were reflected in the alpha diversity of the gut microbiota. A decline in Observed-ASV and Chao1 indices in the Mod group suggested reduced gut microbiota species richness in T2DM model rats. The Shannon and Simpson indices, reflecting species diversity, indicated that community heterogeneity in the HDLC group exceeded that of the other groups. When the results of β-diversity were evaluated by PCA and NMDS, the microbiota composition of T2DM rats was modified considerably. The gut microbiota returned to normal after DLC intervention. Predominant gut microbiota at the phylum level in rats consist primarily of p__Firmicutes, p__Bacteroidetes, and p__Actinobacteria. The stability of these phyla is crucial for immune regulation, energy metabolism, and material metabolism. They are critical for sustaining human health and act as indicators of the organism's internal environment[20]. Compared with the Con group, p__Bacteroidetes significantly increased. p__Actinobacteria also increased. p__Firmicutes significantly decreased in Mod group rats. The p__Bacteroidetes are a group of Gram-negative bacteria with lipopolysaccharide (LPS) as the outermost layer of the cell wall. When the intestinal barrier is disrupted, the LPS enters the blood circulation and triggers systemic chronic nonspecific inflammation associated with obesity and diabetes, among others. Through multi-level species composition analysis, the study found that the abundance of p_Actinobacteria in the intestines of diabetic model rats increased, especially with the proliferation of g__Bifidobacterium. The prevalence of g__Bifidobacterium showed a significant positive association with FBG and IR. As a core member of the acetate-producing functional flora, its excessive proliferation led to abnormal accumulation of intestinal acetate, suggesting that the imbalance of acetate metabolism mediated by it may be a potential mechanism for metabolic disorders in diabetes[21]. The proportion of p__Bacteroidetes and p__Actinobacteria decreased after the intervention of the DLC, suggesting that DLC was able to ameliorate diabetes-induced intestinal dysbiosis to a certain extent. At the genus level, the proportion of g__Ligilactobacillus in the Mod group significantly decreased, while the proportion of g__Prevotella_9 and g__Alloprevotella significantly increased in relative abundance. g_Ligilactobacillus is a probiotic and has a positive effect on maintaining intestinal health. According to the literature, g_Ligilactobacillus is closely related to T2DM[22]. g__Ligilactobacillus enhances intestinal barrier function by promoting the production of SCFAs. Particularly, butyric acid, as the main energy source for colonocytes[23], can improve intestinal barrier integrity by increasing tight junction protein levels, minimizing endotoxin movement, and lowering systemic inflammation[24]. Butyric acid not only enhance insulin secretion but inhibit hepatic gluconeogenesis by activating G-protein-coupled receptors, thus improving insulin sensitivity[25]. This research suggests that the restoration of butyric acid levels in the HDLC group bolsters the hypothesis, implying that g__Ligilactobacillus may contribute to the positive impact of DLC on improving insulin sensitivity by influencing SCFA production. An increase in the number of g__Prevotella_9 and g__Alloprevotella is associated with local and systemic diseases. Diabetes, as a typical metabolic disease, is highly susceptible to the impact of metabolic disorders during its development and progression[26]. DLC intervention may regulate these beneficial microbiotas in multiple ways. It might provide specific nutrients that g__Ligilactobacillus prefers, promoting its growth and proliferation. Also, DLC could modulate the intestinal environment, such as pH and redox potential, creating a more favorable habitat for g__Ligilactobacillus while suppressing the growth of harmful bacteria like g__Prevotella_9 and g__Alloprevotella. This helps maintain the balance of gut microbiota and achieve the therapeutic effect on diabetes.

Research indicates that enhancing the microbiota that produces SCFAs, which are essential metabolites of the gut microbiota, can notably enhance various metabolic conditions, including diabetes and obesity[27]. Butyric acid is a primary energy provider for gut epithelial cells[17] and exerts multiple beneficial effects on diabetes. Butyric acid enhances gut barrier integrity by elevating tight junction proteins, which decreases the movement of endotoxins like LPS and the resulting systemic inflammation[28]. Diminishing systemic inflammation is key to boosting insulin response in peripheral areas like fat and muscle[29]. Butyric acid activates G protein-coupled receptors (GPR41/43), which are present in intestinal epithelial and immune cells. This, in turn, triggers a surge in the release of glucagon-like peptide-1 (GLP-1) and peptide YY (PYY). This process enhances insulin secretion while also suppressing the production of glucose in the liver[30]. AMPK is vital for metabolic regulation as it improves insulin response via enhanced glucose uptake and fatty acid breakdown. Prior research indicates that butyric acid stimulates the AMPK signaling mechanism[31]. The intervention of DLC promotes the restoration of butyric acid levels in the intestine, indicating that DLC can improve insulin sensitivity by improving gut barrier integrity and reducing systemic inflammatory responses. Acetate exerts effects on lipid metabolism, glucose homeostasis, immunity, and body weight in both animals and humans. It achieves these effects by modulating insulin sensitivity and lipid metabolism pathways[32]. Acetic acid stimulates the parasympathetic pathway, boosting GLP-1 and PYY release, which improves insulin production and curbs hunger[31]. In addition, acetic acid diminishes fat breakdown in adipose tissue, curtailing free fatty acid output and thereby enhancing insulin response[33]. Some studies have found that the significant increase in acetic acid concentration in diabetic rats may be due to IR and impaired glucose metabolism, promoting cholesterol biosynthesis and lipogenesis[32]. Cholesterol biosynthesis and lipogenesis are regulated by acetyl coenzyme A converted from acetic acid[34]. The DLC intervention reduced the level of acetic acid, indicating its potential to regulate lipid metabolism and prevent excessive cholesterol production. Propionic acid can effectively alleviate high-fat diet-induced IR. It activates intestinal gluconeogenesis through the gut-brain neural circuit, thereby reducing hepatic glucose output and further improving insulin sensitivity[35]. PPAR-γ is an important regulator of lipid metabolism and insulin sensitivity. Research indicates propionic acid's activation of the PPAR-γ pathway[36]. In addition, propionic acid, as a precursor substance for hepatic gluconeogenesis, further contributes to blood glucose control[37]. The increase in propionic acid levels observed in diabetic rats may be due to abnormal glucose metabolism in the intestine. High concentrations of glucose can promote the production of propionic acid[38]. The DLC intervention reduced the level of propionic acid, indicating that it plays a role in restoring glucose homeostasis. Furthermore, studies have revealed a connection between gut microbiota, SCFAs, and GLP-1 secretion enhancement. As is well-known, GLP-1 is crucial for managing IR and maintaining stable blood sugar levels. This research demonstrated that the DLC intervention notably influenced SCFA levels in the diabetic model. Specifically, it reduced the abnormal elevation of acetic acid and propionic acid and simultaneously partially restored the level of butyric acid. These changes are intimately tied to the reshaping of the gut microbiota and the knock-on benefits for metabolic function. This suggests that the compound may well play a pivotal role in managing diabetes by modulating the SCFA metabolic pathway. DLC possibly influences AMPK, PPAR-γ, and GLP-1 pathways indirectly, modulating gut microbiota and related metabolites.

Metabolomic analyses indicate altered amino acid profiles frequently coincide with T2DM in rodents. Citrulline and ornithine play crucial roles in the urea cycle. Citrulline is a product of the combination of carbon dioxide, ammonia, and ornithine in the liver. Studies indicate that supplementation with L-citrulline can significantly reduce the FBG concentration and glycated hemoglobin level in patients with T2DM[39], suggesting the importance of citrulline metabolism in diabetes, which is consistent with the results of this experiment. As a crucial component of the urea cycle, ornithine aids in ammonia detoxification and modulates the host's lipid metabolism. Studies have found that L-ornithine can reduce IR and improve glucose and lipid metabolism in obese rats[40], suggesting ornithine may aid diabetes management and complication mitigation. Histidine is a vital amino acid for human health. Abnormal metabolism of histidine may lead to impaired function of pancreatic islet β cells and further trigger hyperglycemia and diabetes. Histidine supplementation can improve IR in obese women with metabolic syndrome[41]. In addition, as an intermediate in the lysine metabolic pathway, abnormal metabolism of N6,N6,N6-trimethyl-L-lysine may be related to the dysfunction of adipose tissue macrophages, thus affecting insulin sensitivity and glucose homeostasis. In the creatine pathway, 4-guanidinobutyric acid is involved in the catabolism of arginine and proline[42]. Creatine features a glycine structure and may mitigate damage by suppressing inflammation, oxidative stress, and the effects of aging[43]. However, excessive creatine can also cause blood glucose fluctuations. Thus, this study noted reduced creatine levels in both the Con and HDLC groups. DLC affects arginine biosynthesis, arginine and proline metabolism, histidine metabolism, alanine, aspartate, and glutamate metabolism, as well as the lysine degradation metabolic pathway by regulating the levels of g__Ligilactobacillus, g__Prevotella_9, and g__Anaerovibrio. The Spearman analysis reveals a striking pattern: Ornithine, 4-guanidinobutyric acid, citrulline, and histidine exhibit a strong positive association with g__Ligilactobacillus but a clear negative link to g__Anaerovibrio and g__Prevotella_9. Conversely, N6,N6,N6-trimethyl-L-lysine and creatine show the opposite trend. These findings suggest that DLC significantly influences diabetes management through amino acid pathway regulation.

The TCA cycle serves as a fundamental route for energy metabolism. When this cycle becomes dysregulated, it can disrupt both glucose and lipid metabolism, potentially leading to IR and diabetes[44]. Arginine is a crucial metabolite in both the urea and TCA cycles, significantly impacting diabetes pathophysiology. Arginine produces nitric oxide (NO) via the NO synthase pathway, where NO is essential for the regulation of vascular function and insulin signaling. Research indicates that disrupted arginine metabolism in T2DM patients may reduce NO levels, potentially causing endothelial dysfunction and IR[45]. Argininosuccinic acid is catalyzed by arginosuccinate lyase to produce fumaric acid and arginine. Fumaric acid then enters the TCA cycle. Research indicates that argininosuccinic acid levels are significantly elevated in T2DM model rats[46], suggesting that disrupted argininosuccinic acid metabolism could significantly influence diabetes progression. α-Ketoglutaric acid, a key component in the TCA cycle, significantly regulates elevated blood sugar by decreasing liver glucose production and blocking amino acid conversion to sugar[47]. DLC affects the metabolic pathways related to the TCA cycle by regulating the levels of g__Ligilactobacillus, g__Prevotella_9, g__Roseburia, and g__Anaerovibrio. Spearman analysis showed that argininosuccinic acid was positively correlated with g__Anaerovibrio and g__Prevotella_9 but negatively correlated with g__Ligilactobacillus. α-Ketoglutaric acid was positively correlated with g__Roseburia, while it was negatively correlated with g__Anaerovibrio and g__Prevotella_9. These results indicate that DLC improves diabetic metabolic disorders by regulating the metabolic pathways related to the TCA cycle.

Other classes of compounds such as purine metabolites (xanthine and 7-methylxanthine) were also altered, suggesting disturbed purine metabolism in T2DM rats. It has been shown that purine metabolites are reduced in T2DM which aligns with the findings of this research[48]. Abnormal purine metabolism may lead to an increase in uric acid levels, thereby triggering oxidative stress and inflammatory responses, and exacerbating IR and the pathophysiology of diabetes. Biotin plays an important role in improving insulin sensitivity and blood glucose control. Research has shown that biotin can enhance insulin sensitivity in patients with T2DM, thus improving IR[49]. This study revealed a significant decline in biotin levels in diabetic model rats, indicating that impaired biotin metabolism could be a critical factor in diabetes progression. According to Spearman's analysis, xanthine and 7-methylxanthine are proportional to g__Ligilactobacillus and inversely proportional to g__Anaerovibrio and g__Prevotella_9. Biotin was inversely proportional to g__Anaerovibrio, g__Prevotella_9, and g__Alloprevotella. Thus, DLC affects purine metabolism and biotin metabolism by regulating g__Ligilactobacillus, g__Anaerovibrio, g__Prevotella_9, and g__Alloprevotella. This suggests that DLC may improve diabetes-related metabolic disorders by restoring purine and biotin metabolism balance.

The limitations of this study can be compared with the findings reported in the literature[50,51]. The long-term safety of DLC is unclear, and a toxicological assessment via toxicity tests is needed to identify potential risks. Due to the heterogeneity of T2DM, the role of DLC in different subtypes hasn't been studied, impeding a full understanding of its therapeutic potential. Moreover, the specific roles of the AMPK, PPAR-γ, and GLP-1 signaling pathways in the improvement of diabetes by DLC remain to be fully elucidated. In future research, we plan to use gene knockout or inhibitor experiments to clarify these roles. Additionally, we will evaluate DLC safety through toxicological experiments, include various animal models and clinical samples of different T2DM subtypes, and use molecular biology and clinical evaluation methods to explore DLC efficacy, providing a basis for its development and clinical application.

CONCLUSION

In conclusion, DLC markedly improves symptoms of metabolic disorders in T2DM model rats, such as weight reduction, increased FBG, dyslipidemia, and diminished glucose tolerance. 16S rDNA sequencing reveals that DLC achieves therapeutic outcomes by altering gut microbiota composition and structure. Targeted metabolomics findings indicate that DLC markedly increases the levels of isobutyric acid, butyric acid, and 2-methylbutyric acid among SCFAs, concurrently causing a decrease in acetic acid and propionic acid levels. Non-targeted metabolomics analysis further confirms that DLC can reverse T2DM-related metabolite abnormalities. These findings elucidate the molecular mechanism by which DLC potentially exerts its hypoglycemic effects through the gut microbiota-metabolite-host axis, providing a crucial theoretical foundation and research directions for the clinical application of DLC. Based on these results, it is recommended that future research focus on clinical trials to evaluate the efficacy and safety of DLC, aiming to provide new therapeutic strategies and a theoretical framework for comprehensive diabetes intervention, thereby driving innovation in diabetes treatment.

ACKNOWLEDGEMENTS

The authors would like to thank the Key Laboratory of TCM Extraction and Purification and Quality Analysis for providing the experimental platforms.