Porous starch microspheres loaded with luteolin exhibit hypoglycemic activities and alter gut microbial communities in type 2 diabetes mellitus mice

Abstract

Luteolin (LUT), a natural flavonoid known for its hypoglycemic properties, is primarily sourced from vegetables such as celery and broccoli. However, its poor stability and low bioavailability in the upper digestive tract hinder its application in the functional food industry. To address these challenges, this study employed porous starch (PS) as a carrier to develop PS microspheres loaded with luteolin (PSLUT), simulating its release in vitro. The research assessed the hypoglycemic effects of LUT in type 2 diabetes mellitus (T2DM) mice both before and after PS treatment. In vitro findings demonstrated that PS improved LUT's stability in simulated gastric fluids and enhanced its in vivo bioavailability, aligning with experimental outcomes. PSLUT administration significantly improved body weight, fasting blood glucose (FBG), oral glucose tolerance test (OGTT), pancreatic islet function, and other relevant indicators in T2DM mice. Moreover, PSLUT alleviated abnormal liver biochemical indicators and liver tissue injury caused by T2DM. The underlying hypoglycemic mechanism of PSLUT is thought to involve the regulation of protein kinase B (AKT-1) and glucose transporter 2 (GLUT-2). After four weeks of intervention, various PSLUT doses significantly reduced the Firmicutes to Bacteroidetes ratio at the phylum level and decreased the relative abundance of harmful bacteria at the genus level, including Acetatifactor, Candidatus-Arthromitus, and Turicibacter. This microbial shift was associated with improvements in hyperglycemia-related indicators such as FBG, the area under the curve (AUC) of OGTT, and homeostasis model assessment of insulin resistance (HOMA-IR), which are closely linked to these bacterial genera. Additionally, Lachnoclostridium, Parasutterella, Turicibacter, and Papillibacter were identified as key intestinal marker genera involved in T2DM progression through Spearman correlation analysis. In conclusion, PS enhanced LUT's hypoglycemic efficacy by modulating the transcription and protein expression levels of AKT-1 and GLUT-2, as well as the relative abundance of potential gut pathogens in T2DM mice. These results provide a theoretical foundation for advancing luteolin's application in the functional food industry and further investigating its hypoglycemic potential.

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1 Introduction

Diabetes mellitus, a complex endocrine and metabolic disorder, is predominantly characterized by hyperglycemia resulting from inadequate insulin secretion or impaired insulin function.¹ In recent years, its incidence has surged alongside rapid economic growth and rising living standards. Data from the International Diabetes Federation (IDF) show that in 2019, approximately 463 million adults aged 20–79 were living with diabetes globally, with China representing 25% of cases (116.4 million), the highest worldwide.² By 2021, the number of adults with diabetes had increased to 537 million, with China contributing 140.9 million cases, maintaining its leading position.³ Over the past two years, the global adult diabetes population rose by 15.98%, while China's increased by 21.05%, with type 2 diabetes mellitus (T2DM) accounting for over 90% of cases.³ Chronic T2DM and prolonged hyperglycemia result in irreversible injury to the liver, intestines, and cardiovascular and cerebrovascular systems, highlighting the urgency of controlling hyperglycemia to improve quality of life and safeguard human health.⁴

The role of gut microbiota in human health has gained significant attention, with numerous studies indicating a strong link between gut microbial diversity and the development of T2DM.^5,6 Disruptions in gut microbiota, particularly changes in carbohydrate metabolism, have been correlated with T2DM progression.^7,8 An elevated the ratio of Firmicutes to Bacteroidetes (Firmicutes/Bacteroidetes) at the phylum level has been associated with impaired carbohydrate metabolism and insulin resistance. At the genus level, increased levels of Alistipe and Ruminiclostridium have been shown to influence short-chain fatty acids (SCFAs) production in T2DM mice, alleviating hyperglycemia. Additionally, variations in the abundance of Lactobacillus, ParaBacteroides, Lachnoclostridium, and Desulfovibrio have been linked to biochemical indicators such as total cholesterol (TC), triglycerides (TG), low density lipoprotein-cholesterol (LDL-c), and high density lipoprotein-cholesterol (HDL-c) in T2DM mice.⁸ Ai et al. reported that fermenting rice bran with Lactobacillus enhanced glucose utilization, reduced lipid accumulation in insulin-resistant HepG-2 cells, lowered blood glucose and lipid levels, and improved antioxidant activity in T2DM mice.⁹ Sedighi et al. found significantly lower levels of Lactobacillus and Bifidobacterium in patients with T2DM compared to healthy individuals, suggesting a negative correlation between these bacteria and the disease.¹⁰ Furthermore, gut microbiota imbalances, particularly a reduction in biodiversity and beneficial bacteria, may compromise the intestinal barrier, exacerbating T2DM and potentially affecting multiple organs.^11,12 As a result, restoring gut microbiota balance has emerged as a promising therapeutic approach for managing T2DM and its complications.

Luteolin (LUT, 3′,4′,5,7-tetrahydroxyflavone), a natural flavonoid found in peanut seed skin, celery, broccoli, and other fruits and vegetables,¹³ has shown potential in regulating fatty liver disease by modulating gut microbiota and altering metabolite levels.¹⁴ However, its sensitivity to metal ions, high oxidative potential, poor stability, and low solubility in the upper digestive tract significantly limit its bioavailability and practical application in medicine. Enhancing LUT's stability during oral delivery to ensure its effective release in the intestines and subsequent modulation of gut microbiota remains a critical research focus.

In recent years, the exploration of novel LUT delivery systems has attracted substantial attention from researchers. Key delivery methods include solid dispersions, liposomal vectors, and microsphere vectors, with the latter showing particular promise in enhancing the bioavailability of lipophilic bioactive compounds.¹⁵ Among these, microsphere vectors are primarily synthesized using porous starch (PS), a non-toxic starch derivative known for its large surface area and strong adsorption capabilities, produced through mild enzymatic hydrolysis of natural corn starch.¹⁶ PS offers numerous advantages over alternative carriers: it is cost-effective, readily available, safe, non-toxic, and biodegradable. Its high pore volume and surface area enable superior adsorption without altering the physicochemical properties of the encapsulated compound. Furthermore, PS microspheres facilitate the slow release of bioactive compounds during digestion, thereby increasing their in vivo efficacy. Several studies have demonstrated the potential of PS as an effective carrier. For example, Wahab et al. encapsulated curcumin and resveratrol within PS, observing both enhanced stability in simulated gastric fluid and sustained release in simulated intestinal fluid.¹⁷ Similarly, Wang et al. highlighted PS's ability to encapsulate naringin, which displayed slow-release behavior in simulated digestive conditions.¹⁸ Oliyaei et al. further investigated PS-loaded fucoxanthin and its anti-diabetic effects on T2DM mice, finding that it reduced fasting blood glucose (FBG) levels, increased plasma insulin levels, and promoted pancreatic β-cell regeneration.¹⁹ These studies underscore PS's remarkable adsorption capacity, leading to its widespread application across food, pharmaceutical, agricultural, and cosmetic industries.

In this study, LUT was selected as the core bioactive compound, with PS employed as the delivery vector to develop PS microspheres loaded with LUT (PSLUT). T2DM mice were used as the experimental model to assess PSLUT's effects on hyperglycemia and gut microbial composition. The results contribute to a theoretical framework for enhancing LUT's bioavailability in functional foods and advancing its potential hypoglycemic applications.

2 Materials and methods

2.1 Chemicals and reagents

Pepsin (≥2500 U mg⁻¹), pancreatin (USP) from porcine pancreas, and trypsin (≥250 U mg⁻¹) were sourced from Shanghai Aladdin Biochemical Technology Co., Ltd (Shanghai, China). LUT (AR), HCl (AR), NaOH (AR), NaCl (AR), KCl (AR), NaHCO₃ (AR), CaCl₂·2H₂O (AR), bile salt (BR), and streptozotocin (USP) were obtained from Sinopharm Group Chemical Reagent Co., Ltd (Beijing, China). Porous corn starch was purchased from Xi'an Baichuan Biotechnology Co., Ltd (Shanxi, China).

2.2 Preparation and in vitro release simulation of PSLUT

LUT (100 mg) was dissolved in 100 mL of acetone and fully dissolved using an ultrasonic shaker until a clear solution was achieved. PS (200 mg) was then added, and the mixture was sonicated for 5 minutes. The solution was subsequently placed in a 37 °C water bath with magnetic stirring for 2 hours to facilitate adsorption. Following this, the mixture was centrifuged at 10 000 rpm for 10 minutes, and the LUT content in the supernatant was measured via HPLC (Agilent 1260 LC). The precipitate was dried at 40 °C for 12 hours to obtain PSLUT. The morphology and structure of PSLUT were characterized by SEM (Nova NanoSEM 450) and FT-IR (NEXUS-670).

A sample of 0.1 g PSLUT was dissolved in 10 mL deionized water and mixed with 10 mL of simulated gastric electrolyte solution (3.1 g L⁻¹ NaCl, 1.1 g L⁻¹ KCl, 0.6 g L⁻¹ NaHCO₃, 0.15 g L⁻¹ CaCl₂·2H₂O, 236 mg L⁻¹ pepsin, pH adjusted to 2.5 with 0.1 M HCl). The mixture was incubated at 37 °C and 80 rpm for 2 hours. The pH was then adjusted to 7.0 with 0.1 M NaOH. A simulated small intestinal electrolyte solution (5.4 g L⁻¹ NaCl, 0.65 g L⁻¹ KCl, 0.3 g L⁻¹ NaHCO₃, 0.25 g L⁻¹ CaCl₂·2H₂O, pH adjusted to 7.0 with 0.1 M NaOH) was added in a 1 : 1 ratio (v/v). At the same time, pancreatic enzyme solution (7%, w/v), bile salt solution (4%, w/v), and 5.2 mg of trypsin were added. The mixture was incubated at 37 °C and 80 rpm for 4 hours, with the pH maintained at 7.0 to simulate small intestinal digestion. Samples (1 mL) of the simulated digestive solution were collected hourly. After boiling the samples to inactivate the proteases, they were centrifuged at 3000 rpm for 20 minutes, and the LUT content in the supernatant was determined. Each treatment was repeated three times to ensure reproducibility. Release rate (%) = content of LUT in solution (g)/total content of LUT in microspheres (g).

2.3 Animal experiment

Sixty male ICR mice (SPF, 4 weeks old, weighing 17–23 g) were obtained from the Comparative Medicine Centre of Yangzhou University (Yangzhou, China). The mice were housed in a controlled, well-ventilated barrier system maintained at 24 ± 2 °C with 50 ± 10% relative humidity and a 12-hour light/dark cycle. They were provided with ad libitum access to a standard chow diet and deionized water. After one week of acclimatization, 10 mice were randomly assigned to the Normal group and continued on a chow diet, while the remaining 50 were fed a high-sucrose, high-fat (HSHF) diet⁸ for four weeks to induce metabolic changes. Following this induction phase, the 50 mice were administered intraperitoneal (i.p.) injections of streptozotocin (STZ) at a dose of 35 mg kg⁻¹, three times a week. The Normal group received the same volume of 0.1 M citrate buffer. Forty-eight hours after the final injection, FBG levels were measured from tail vein blood, and mice with FBG levels exceeding 11.1 mmol L⁻¹ were classified as T2DM models. The 50 T2DM mice were then randomly allocated into five groups: Model group (n = 10), PSLUT-L group (low dose of PSLUT, 50 mg kg⁻¹ d⁻¹, n = 10), PSLUT-H group (high dose of PSLUT, 100 mg kg⁻¹ d⁻¹, n = 10), LUT group (luteolin, 100 mg kg⁻¹ d⁻¹, n = 10), and MET group (metformin hydrochloride, 100 mg kg⁻¹ d⁻¹, n = 10). Both the Normal and Model groups were administered deionized water (100 mg kg⁻¹ d⁻¹) intragastrically. All mice had unrestricted access to water and diet throughout the 4-week treatment period. Daily monitoring of fur condition, activity levels, and general behavior was conducted. Weekly assessments of body weight and FBG were performed, and an oral glucose tolerance test (OGTT) was carried out after the 4-week treatment period.

For the OGTT, FBG (G_{0 h}) was measured using a glucose meter before administering a glucose solution (2 g per kg body weight) intragastrically. Blood glucose levels were subsequently recorded at 0.5 h, 1 h, and 2 h post-administration. After overnight fasting, mice were anesthetized with Sutazine (55 mg per kg body weight) and cyrazine hydrochloride (5 mg per kg body weight). Blood samples were collected via enucleation and cervical dislocation, followed by centrifugation (2000 rpm, 10 min) to obtain serum for biochemical analyses. Portions of the liver and cecum tissues were excised, with some samples fixed in 4% paraformaldehyde for histopathological examination, and others stored at ultra-low temperatures for further analysis. The study adhered to the “Guide for the Care and Use of Laboratory Animals” as published by the National Institutes of Health (Publication no. 85-23, Rev. 1985), and the experimental protocol was approved by the Lab Animal Ethical Committee of Jiangsu Vocational College of Medicine (approval number: SYLL-2023-704).

2.4 Pancreatic islet β cell function and insulin correlation indices

Fasting insulin (FINs), interleukin-6 (IL-6), and interleukin-10 (IL-10) levels were measured according to the manufacturer's instructions for ELISA kits (Chundu, China). The homeostasis model assessment (HOMA) was used to evaluate the balance between glucose metabolism and insulin function,²⁰ including HOMA-pancreatic islet β cell function (HOMA-β) = 20 × FINs/(FBG − 3.5), HOMA-insulin resistance (HOMA-IR) = FINs × FBG/22.5, and HOMA-insulin sensitivity (HOMA-IS) = 1/(FINs × FBG).²¹

2.5 Serum and liver biochemical indicators

Serum samples were prepared as outlined in section 2.3. Liver tissues were rinsed with normal saline and homogenized in a 1 : 9 (w/v) ratio with normal saline. The supernatant from the homogenized liver tissues was collected for analysis. Levels of TC, TG, LDL-c, and HDL-c in both serum and liver supernatant were measured according to the instructions provided with the corresponding assay kits (Jiancheng, China).

2.6 Histopathological analysis

Liver tissues fixed in 4% paraformaldehyde were dehydrated through a graded ethanol series, cleared in xylene, and embedded in paraffin. Sections were cut at a thickness of 4 μm using a microtome (Nikon, Japan) and placed on slides. These sections were baked at 40 °C until the paraffin melted. Hematoxylin–eosin staining was performed, and the stained slices were sealed with neutral resin. Digital images of the liver sections were captured using an optical microscope (Nikon, Japan).

2.7 qPCR, western blotting, and immunohistochemistry

Total RNA was extracted from liver tissues using TRIzol reagent (Invitrogen, USA), and cDNA synthesis was conducted using the RevertAid First Strand cDNA Synthesis Kit (Takara, Japan). Quantitative PCR (qPCR) was carried out with SYBR® Premix Ex Taq™ II (Takara, Japan) on an ABI 7500 fluorescence quantitative PCR system (Applied Biosystems, USA). The transcription levels of protein kinase B (AKT-1) and glucose transporter 2 (GLUT-2) mRNA were quantified, and relative expression was calculated using the 2^−ΔΔCt method, with β-actin serving as the internal reference. The specific primers used are listed in Table 1.

Table 1 List of all primers used for qPCR

Primer name	Forward primer (5′–3′)	Reverse primer (5′–3′)
β-Actin	TGTCCACCTTCCAGCAGATGT	AGCTCATAACAGTCCGCCTAGA
AKT-1	ACTCATTCCAGACCCACGAC	CCGGTACACCACGTTCTTCT
GLUT-2	TACGGCAATGGCTTTATC	CCTCCTGCAACTTCTCAAT

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Liver proteins were isolated by homogenizing the tissue, followed by SDS-PAGE gel electrophoresis. Proteins were transferred to a PVDF membrane (Sangon, China) and blocked with QuickBlock™ solution at 4 °C for 40 minutes. Membranes were then incubated overnight with AKT-1 and GLUT-2 primary antibodies (Sangon, China). The next day, the membranes were exposed to HRP-conjugated goat anti-rabbit IgG at 37 °C for 1.5 hours. Protein bands were visualized using the GeneGnome XRQ Chemiluminescence Imaging system (Syngene, UK), and gray value analysis was performed using Image J 1.8.0 (Bethesda, USA).

For immunohistochemistry, liver sections were dewaxed and treated with 3% hydrogen peroxide for 15 minutes to quench endogenous peroxidase activity. After washing with deionized water and performing antigen retrieval, the sections were incubated overnight at 4 °C with rabbit polyclonal antibodies against AKT-1 and GLUT-2. The next day, the sections were incubated with HRP-labeled goat anti-rabbit IgG for 30 minutes and counterstained with hematoxylin. The stained sections were examined under an image analysis system (Nikon Eclipse TE2000-U, Japan), and a semi-quantitative histochemistry score (H-Score) was calculated for each section following established formulas.²²

2.8 Analysis of gut microbiota

Cecal contents (0.1 g per mouse) were collected for microbiota analysis. Total microbial genomic DNA was extracted from the cecal contents using the QIAamp DNA Stool Mini Kit (Qiagen, Germany), and DNA integrity was evaluated through 2% agarose gel electrophoresis. Primers targeting the V3–V4 hypervariable regions were used for PCR amplification, specifically the 341F (“5′-CCTAYGGGRBGCASCAG-3′”) and 806R (“5′-GGACTACNNGGGTATCTAAT-3′”) sequences. The amplified PCR products were sequenced on the Illumina NovaSeq PE250 platform. To analyze the relationship between hyperglycemic parameters and gut microbiota at the genus level, Spearman's correlation analysis was performed using Wekemo Bioincloud. Additionally, the correlation network of parameters with a strong correlation (|r| > 0.6) was visualized using Cytoscape software (version 3.9.0).

2.9 Determination of short-chain fatty acids (SCFAs) in cecum

Standard solutions of SCFAs were prepared in a 10 mL volumetric flask and diluted with anhydrous ether to create single standard reserve solutions, which were further diluted into six standard concentrations for gas chromatography analysis (GC-2010Plus, Shimadzu, Japan). Standard curves were generated based on the peak area and the concentration of each SCFA. Cecal content samples (100 mg) were homogenized in deionized water at a solid–liquid ratio of 1 : 10 (w/v). Phosphoric acid was added, followed by vortexing for 2 minutes. SCFAs were extracted with anhydrous ether, filtered through a 0.22 μm nylon filter, and analyzed by gas chromatography.

The gas chromatography conditions included an HP-INNOWAX capillary column (30 m × 0.25 mm × 0.25 μm) and high-purity nitrogen as the carrier gas, with a column flow rate of 1 mL min⁻¹. The injection volume was set at 1 μL, with the inlet and detector temperatures both maintained at 260 °C and a split flow ratio of 10 : 1. The temperature program began at 100 °C, held for 1 minute, then increased to 200 °C at a rate of 5 °C min⁻¹, and held for 2 minutes.

2.10 Statistical analysis

All experimental data were presented as mean ± standard deviation. Statistical analyses were performed using SPSS 16.0 (IBM, New York, USA). The Kruskal–Wallis test was applied, followed by Dunn's post hoc test with Bonferroni's correction, to determine significance. A p-value of <0.05 was considered statistically significant.

3 Results

3.1 The structural morphology and released simulation of PSLUT

At 500× and 5000× magnifications, LUT (Fig. 1A) exhibited a lamellar structure. In contrast, PS (Fig. 1B) displayed a hollow structure with numerous internal cavities, which allowed it to adsorb and encapsulate LUT, providing protective and controlled release properties. In PSLUT (Fig. 1C), LUT was observed to be adsorbed within the PS and around the surface cavities, effectively achieving the purpose of load. As shown in Fig. 1D, the H–O–H bending vibration peak of PSLUT at 1617.84 cm⁻¹ shifted to 1653.92 cm⁻¹ compared to PS alone, indicating the interaction between the hydrophilic and lipophilic groups of PS and LUT, leading to a broadening of the peak.²³ Additionally, the disappearance of LUT's O–H flexural stretching vibration peaks at 683.08 and 637.50 cm⁻¹ confirmed the successful loading of LUT by PS. Furthermore, the increased intensity of the C–O–C absorption peak in glucose at 1020.69 cm⁻¹ in PSLUT suggested potential hydrogen bonding interactions between LUT and the glucose molecules in PS.^24,25

Fig. 1 SEM images of LUT (A), PS (B) and PSLUT (C) at 500× and 5000× magnification, respectively; FT-IR image of LUT, PS and PSLUT (D); simulated continuous release rate in gastrointestinal tract (%) (E).

During simulated gastrointestinal release (Fig. 1E), the LUT release rate in gastric juices was 29.7% after 2 hours. However, after 4 hours in simulated intestinal fluid, the release rate reached 85.3% by 6 hours. These results demonstrate that PSLUT released most of the luteolin in the alkaline intestinal environment, thereby achieving effective targeted delivery.

3.2 Effects of PSLUT on bodyweight abnormality and blood glucose-related indicators in T2DM mice

Fig. 2A presents the experimental timeline for the animal experiment. At the start of the experiment, all groups, except the Normal group, showed a significant reduction in body weight compared to the Normal group (p < 0.05) (Fig. 2B). After two weeks of PSLUT treatment, the body weights of mice in the Normal, PSLUT-L, PSLUT-H, and LUT groups were significantly higher than those in the Model group (p < 0.05). Additionally, the body weight of mice in the PSLUT-H group was significantly greater than in the MET group (p < 0.05). After four weeks of treatment, the body weights of mice in the PSLUT-L, PSLUT-H, and LUT groups remained significantly higher than those in the Model and MET groups (p < 0.05), though still significantly lower than the Normal group (p < 0.05).

Fig. 2 (A) The timeline of animal experiment design; effects of PSLUT intervention during the experimental period: (B) body weight, (C) FBG, (D) OGTT, (E) AUC of OGTT, and (F) serum GSP. PSLUT, porous starch microspheres loaded with luteolin; T2DM, type 2 diabetes mellitus; WK, week. Different superscript letters indicate statistically significant differences between the groups (p < 0.05).

Fig. 2C illustrates the FBG level in T2DM mice during the PSLUT treatment period. At the start of the experiment (0 weeks), FBG level was significantly elevated in all groups compared to the Normal group (p < 0.05). After two weeks of PSLUT treatment, the FBG level in the PSLUT-L, PSLUT-H, LUT, and MET groups were significantly higher than in the Normal group (p < 0.05) but significantly lower than in the Model group (p < 0.05).

Fig. 2D shows the glucose tolerance test in T2DM mice. After administering 2 g per kg body weight of glucose, the blood glucose level peaked at 0.5 h before gradually decreasing. At 0 h, the blood glucose level in the Normal group was significantly lower than in all other groups (p < 0.05), while the Model group exhibited significantly higher level than the other groups (p < 0.05). At 0.5 h, all groups reached their maximum glucose level, with the PSLUT-L, PSLUT-H, and MET groups showing significantly lower levels than the Model group (p < 0.05). Although the LUT group had lower glucose level than the Model group, this difference was not statistically significant (p > 0.05). At 1 and 2 h, glucose levels gradually declined across all groups, with those in the PSLUT-L, PSLUT-H, LUT, and MET groups remaining significantly higher than the Normal group (p < 0.05) but significantly lower than the Model group (p < 0.05).

The area under the curve (AUC) for the OGTT, shown in Fig. 2E, was used to measure glucose regulation in each group. The AUC value in the PSLUT-L, PSLUT-H, LUT, and MET groups were significantly reduced compared to the Model group (p < 0.05). Furthermore, the AUC in the PSLUT-H group was significantly lower than in the PSLUT-L and LUT groups (p < 0.05). Fig. 2F shows the serum GSP level. The GSP level in the Model, PSLUT-L, LUT, and MET groups were significantly elevated compared to the Normal group (p < 0.05), while the PSLUT-H group showed a slight but non-significant increase (p > 0.05). However, compared to the Model group, the GSP level in the PSLUT-L, PSLUT-H, LUT, and MET groups were significantly decreased (p < 0.05), with the PSLUT-H group showing significantly lower GSP level than the LUT group (p < 0.05).

3.3 Effects of PSLUT on pancreas islet function index and serum interleukin in T2DM mice

HOMA is a widely utilized method for evaluating pancreatic islet function, encompassing β cell activity, insulin resistance, and insulin sensitivity. As depicted in Fig. 3A, HOMA-β value in all treatment groups were significantly elevated compared to the Model group (p < 0.05), with the PSLUT-H group showing notably higher HOMA-β level than the PSLUT-L, LUT, and MET groups (p < 0.05). Fig. 3B illustrates that HOMA-IR value was significantly lower in the Normal, PSLUT-L, PSLUT-H, LUT, and MET groups relative to the Model group (p < 0.05), with the PSLUT-H group displaying significantly lower HOMA-IR than the PSLUT-L and LUT groups (p < 0.05). In Fig. 3C, HOMA-IS value in the Normal group was significantly higher than in the other groups (p < 0.05), and the PSLUT-H group showed a significant improvement in HOMA-IS value compared to the Model group (p < 0.05). As shown in Fig. 3D, IL-6 value in the Model group was significantly higher than in the other groups (p < 0.05), and the PSLUT-H group showed a significant improvement in IL-6 value compared to the LUT and MET groups (p < 0.05). In Fig. 3E, IL-10 value in the Model group was significantly lower than in the other groups (p < 0.05), and the PSLUT-H group showed a significant improvement in IL-10 value compared to the PSLUT-L and LUT groups (p < 0.05).

Fig. 3 Homeostatic model assessment insulin correlation index and serum biochemical indicators: (A) HOMA-β, (B) HOMA-IR, (C) HOMA-IS, (D) IL-6, (E) IL-10, (F) serum TC, (G) serum TG, (H) serum LDL-c, (I) serum HDL-c, and (J) serum LDL-c/HDL-c.

3.4 Effects of PSLUT on serum biochemical indicators in T2DM mice

T2DM not only manifests in hyperglycemia but also disrupts serum biochemical markers in mice. As presented in Fig. 3F, serum TC level was significantly reduced across all treatment groups compared to the Model group (p < 0.05), with the PSLUT-L group exhibiting lower TC level than the MET group (p < 0.05), and the PSLUT-H group showing significantly reduced TC level compared to both the LUT and MET groups (p < 0.05). Similarly, Fig. 3G shows that serum TG level was markedly lower in all treatment groups than in the Model group (p < 0.05), with the PSLUT-H group displaying significantly lower TG level than the PSLUT-L, LUT, and MET groups (p < 0.05).

Fig. 3H reveals that serum LDL-c level in the Model group was significantly higher than in the Normal, PSLUT-L, PSLUT-H, LUT, and MET groups (p < 0.05), while serum HDL-c level was significantly lower in the Model group compared to the other groups (p < 0.05) (Fig. 3I). Since individual serum biochemical markers do not fully reflect the risk of T2DM, the LDL-c/HDL-c ratio is considered a reliable predictor of metabolic disorders. Fig. 3J demonstrates that the LDL-c/HDL-c ratio in the Normal, PSLUT-L, PSLUT-H, LUT, and MET groups were significantly reduced compared to the Model group (p < 0.05).

3.5 Effects of PSLUT on liver biochemical indicators in T2DM mice

When lipid metabolism disorders occur, the liver's lipid synthesis capacity increases, leading to fat accumulation, oxidative stress, and the release of inflammatory and pro-apoptotic factors. This process can result in varying degrees of liver injury and pose significant health risks. As illustrated in Fig. 4A–C, liver levels of TC, TG, and LDL-c in all treatment groups were significantly lower than in the Model group (p < 0.05). Notably, Fig. 4C shows that the liver LDL-c level in the PSLUT-H group was significantly lower than in the MET group (p < 0.05). Conversely, Fig. 4D demonstrates that liver HDL-c level was significantly lower in the Model group compared to other groups (p < 0.05), with the PSLUT-H group exhibiting significantly higher level than the PSLUT-L, LUT, and MET groups (p < 0.05). Additionally, Fig. 4E shows that the liver LDL-c/HDL-c ratio in all treatment groups were significantly lower than in the Model group (p < 0.05), with the PSLUT-H group having a significantly lower ratio than the MET group (p < 0.05).

Fig. 4 Biochemical indicators and histopathological analysis of liver tissue: (A) liver TC, (B) liver TG, (C) liver LDL-c, (D) liver HDL-c, (E) liver LDL-c/HDL-c, (F) liver tissue (400× magnification), and (G) liver tissue score.

3.6 Effects of PSLUT on liver histopathology in T2DM mice

Histopathological analysis further highlighted the extent of liver injury in T2DM mice (Fig. 4F). In the Normal group, hepatic cords were arranged neatly in a radial pattern, with clear nuclei, intact cellular structures, and no signs of lesions. In contrast, the Model group exhibited disordered hepatic cords and diffuse hepatic steatosis, marked by numerous lipid droplet vacuoles in hepatocytes, uneven nuclei, disrupted cell membranes, and inflammatory cell infiltration. While the PSLUT-L group showed a noticeable reduction in lipid droplet vacuoles compared to the Model group, hepatic cords remained disordered. In the PSLUT-H group, liver morphology improved significantly, with fewer lipid droplet vacuoles, clearer hepatocyte structure, and well-defined hepatic cords. The LUT and MET groups also exhibited some lipid droplet vacuoles. The tissue scoring system applied in this study, based on the method by Kleiner et al.,²² evaluated five key categories: hepatocyte steatosis, inflammatory lesions in hepatic lobules, hepatocyte damage and fibrosis, lipid droplet vacuoles, and miscellaneous features. Each category was graded on a scale of 0 to 3, with higher scores indicating greater severity. The total score was obtained by summing the individual category scores (Fig. 4G). Compared to the Model group, tissue scores were significantly reduced in all other groups (p < 0.05). The PSLUT-L and PSLUT-H groups had significantly lower scores than the LUT and MET groups (p < 0.05), with the PSLUT-H group showing the lowest tissue scores overall, significantly lower than the PSLUT-L group (p < 0.05).

3.7 qPCR, western blotting, and immunohistochemical analysis

This study explored the effects of PSLUT on the relative transcription and protein expression levels of AKT-1 and GLUT-2 in the liver. As depicted in Fig. 5A and B, the transcription levels of AKT-1 and GLUT-2 in the Normal, PSLUT-L, PSLUT-H, LUT, and MET groups were significantly elevated compared to the Model group (p < 0.05). Additionally, the transcription levels of both AKT-1 and GLUT-2 in the PSLUT-H group were significantly higher than in the LUT and MET groups (p < 0.05), while only the transcription level of GLUT-2 in the PSLUT-L group showed a significant increase (p < 0.05). In Fig. 5D and E, the protein expression levels of AKT-1 and GLUT-2 in the PSLUT-L and PSLUT-H groups were also significantly higher than in the Model group (p < 0.05). Moreover, the PSLUT-H group exhibited notably higher protein expression levels of AKT-1 and GLUT-2 compared to the PSLUT-L, LUT, and MET groups (p < 0.05).

Fig. 5 Effects of PSLUT on mRNA transcription and protein expression levels of AKT-1 and GLUT-2. (A and B) qPCR analysis; (C, D and E) western blotting analysis. Immunohistochemical analysis and H-score of (F and G) AKT-1, and (H and I) GLUT-2.

Immunohistochemistry, a widely used technique in pathological diagnostics, was employed to assess the expression levels of AKT-1 and GLUT-2 in liver tissue. This method allows for the detection of antigen amounts in tissue sections through specific antibody–antigen interactions. The H-Score, which converts the number of positive cells and staining intensity into quantifiable values, was used for qualitative and relative quantification of antigen distribution in hepatocytes. As shown in Fig. 5F and H, after 4 weeks of PSLUT treatment, AKT-1 and GLUT-2 were expressed in liver tissue with a brownish-yellow hue, primarily located in the cytoplasm or nucleus. In contrast, the Model group displayed reduced expression of both proteins. Fig. 5G and I reveals that the H-Score for AKT-1 and GLUT-2 in the Normal, PSLUT-L, PSLUT-H, LUT, and MET groups were significantly higher than in the Model group (p < 0.05). Furthermore, the PSLUT-L and PSLUT-H groups demonstrated significantly higher H-Scores compared to the LUT and MET groups (p < 0.05), with the PSLUT-H group showing a significantly higher H-Score than the PSLUT-L group (p < 0.05) for both AKT-1 and GLUT-2.

3.8 The composition of gut microbiota and the content of short-chain fatty acids

The Firmicutes/Bacteroidetes ratio, a key indicator of gut microbiota composition at the phylum level, is strongly associated with hyperglycemia in T2DM mice. Fig. 6A and B presents the alterations in the relative abundance of microbiota at the phylum level in T2DM mice treated with PSLUT. As depicted in Fig. 6A, the relative abundance of Firmicutes was reduced in the PSLUT-L, PSLUT-H, LUT, and MET groups, while the relative abundance of Bacteroidetes was increased, compared to the Model group. Fig. 6B demonstrates that the Firmicutes/Bacteroidetes ratio significantly decreased in the Normal, PSLUT-L, PSLUT-H, LUT, and MET groups compared to the Model group (p < 0.05).

Fig. 6 Effects of PSLUT intervention on intestinal microbial communities in the cecum: (A) diversity of gut microbiota at the phylum level, (B) Firmicutes/Bacteroidetes ratio. Extended error bar plot showing intestinal microbiota with significant differences at the genus level between the groups: (C) PSLUT-H (blue) vs. Model (orange); (D) PSLUT-L (green) vs. Model (orange).

At the genus level, further analysis revealed notable differences in the PSLUT-L and PSLUT-H groups compared to the Model group. Fig. 6C shows that in the PSLUT-H group, the relative abundance of Bifidobacterium and Alistipes significantly increased (p < 0.05), while the relative abundance of Acetatifactor, Candidatus-Arthromitus, Papillibacter, Marvinbryantia, Turicibacter, Blautia, Faecalibaculum, and Candidatus-Stoquefichus significantly decreased (p < 0.05). Additionally, Fig. 6D indicates that in the PSLUT-L group, the relative abundance of Acetatifactor, Angelakisella, Candidatus-Arthromitus, Intestinimonas, Parvibacter, Turicibacter, Anaerotruncus, Unidentified-Ruminococcaceae, and Acetitomaculum was significantly lower than in the Model group (p < 0.05). Notably, the relative abundance of Acetatifactor, Candidatus-Arthromitus, and Turicibacter were significantly reduced in both the PSLUT-H and PSLUT-L groups compared to the Model group (p < 0.05). These results suggest that PSLUT treatment modulates gut microbiota composition, contributing to improvements in hyperglycemia-related symptoms in T2DM mice.

SCFAs, such as acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, and valeric acid, are key metabolites produced by gut microbiota during the fermentation of carbohydrates. Fig. 7A–G illustrates changes in SCFA levels in the intestinal contents of T2DM mice treated with PSLUT. Compared to the Model group, the levels of acetic acid, propionic acid, butyric acid, isovaleric acid, valeric acid, and total SCFAs were significantly elevated in both the PSLUT-L and PSLUT-H groups (p < 0.05). Notably, Fig. 7C shows that the isobutyric acid content was significantly higher in the PSLUT-H and Normal groups compared to the Model group (p < 0.05). A correlation heat map was generated to further explore the relationship between bacterial genera and SCFAs. As seen in Fig. 7H, Bifidobacterium and Alistipes exhibited a significant positive correlation with several SCFAs, including acetic acid, propionic acid, butyric acid, isovaleric acid, valeric acid, and total SCFAs (p < 0.05). Conversely, Turicibacter showed a significant negative correlation with acetic acid, isobutyric acid, butyric acid, isovaleric acid, valeric acid, and total SCFAs (p < 0.05). Additionally, Akkermansia displayed a negative correlation with acetic acid, propionic acid, butyric acid, isovaleric acid, and total SCFAs (p < 0.05). The Mantel test was used to analyze the correlation between hypoglycemic parameters and SCFAs (Fig. 7I), revealing that hypoglycemic markers such as serum GSP, serum TC, serum TG, serum LDL-c, serum LDL-c/HDL-c, liver TG, liver HDL-c, and HOMA-IR were strongly associated with acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, and valeric acid (p < 0.001).

Fig. 7 Effects of PSLUT intervention on the contents of SCFAs in the cecum of mice: (A) acetic acid, (B) propionic acid, (C) isobutyric acid, (D) butyric acid, (E) isovaleric acid, (F) valeric acid, (G) total SCFAs, (H) Spearman correlation heat map between bacterial genera and SCFAs, (I) Mantel test analysis is used to determine the correlation between hypoglycemic parameters and SCFAs. SCFA, short-chain fatty acids.

Furthermore, the relationship between specific differential bacterial genera and hypoglycemia-related indicators following PSLUT treatment was examined (Fig. 8A). Bifidobacterium was significantly positively correlated with serum HDL-c, liver HDL-c, and AKT-1 (p < 0.05), and negatively correlated with liver TC, IL-6, and serum TC (p < 0.05). Similarly, Alistipes showed a significant negative correlation with liver TC (p < 0.05). On the other hand, Acetatifactor, Candidatus-Arthromitus, and Turicibacter were positively correlated with serum LDL-c, liver TC, IL-6, serum GSP, serum TC, liver LDL-c, serum TG, HOMA-IR, FBG, and the AUC of OGTT (p < 0.05). These bacterial genera were negatively correlated with serum HDL-c, GLUT-2, IL-10, HOMA-β, and AKT-1 (p < 0.05). As illustrated in Fig. 8B, parameters with a correlation coefficient |r| ≥ 0.6 were screened for further analysis. Candidatus-Arthromitus demonstrated a strong negative correlation with HOMA-β, GLUT-2, and IL-10 (r = −0.823, −0.762, and −0.751, respectively), and a strong positive correlation with serum GSP, liver LDL-c, liver TG, and serum TG (r = 0.734, 0.722, 0.715, and 0.702, respectively). Additionally, other bacterial genera such as Lachnoclostridium, Parasutterella, Turicibacter, and Papillibacter showed significant correlations with various hypoglycemic parameters. For example, Parasutterella exhibited a strong negative correlation with FBG and OGTT levels (r = −0.677 and −0.637, respectively), while Papillibacter demonstrated a strong positive correlation with serum TC, serum TG, FBG, and OGTT levels (r = 0.606, 0.67, 0.682, and 0.636, respectively).

Fig. 8 Hierarchical clustering analysis (A) and visualization of the correlation network (B) using Spearman correlation of hypoglycemic parameters and representative bacterial genus. In (A), Indian red and forest green indicate positive and negative correlation, respectively, and the depth of color indicates the strength of correlation. In (B), violet red, green, and light sky blue indicate hypoglycemic parameters, bacterial genus, and short-chain fatty acids, respectively. Blue solid lines, r > 0.6, adjusted p < 0.01; red dotted lines, r < −0.6, adjusted p < 0.01.

4 Discussion

T2DM is a widespread endocrine and metabolic disorder that presents a serious health risk. A prolonged HSHF diet can elevate FBG levels, impair OGTT results, and disrupt pancreatic islet function. Moreover, persistent hyperglycemia contributes to lipid deposition and inflammatory in liver tissue. LUT has been shown to mitigate hepatic inflammation by modulating gut microbiota composition and influencing bacterial metabolite levels. However, LUT's inherent instability and poor water solubility limit its bioavailability, constraining its use in biological and pharmacological applications.

In this study, PSLUT was developed, using LUT as the active compound and corn-derived PS as the carrier, and administered to T2DM mice in different dosages via intragastric delivery. After 4 weeks of treatment, the body weights of the PSLUT-L and PSLUT-H groups significantly improved, suggesting that PSLUT enhances energy absorption and utilization in T2DM mice, thereby preventing abnormal weight loss. Interestingly, the MET-treated group did not exhibit significant body weight recovery at either 2 or 4 weeks, with body weight by week 4 falling below that of the Model group. This outcome implies that MET may inhibit intestinal glucose absorption, contributing to weight loss in T2DM mice.²⁰ FBG is commonly used to diagnose T2DM. In this study, FBG level significantly decreased after 2 and 4 weeks of PSLUT treatment, showing a pattern similar to that of MET. Notably, after 4 weeks of PSLUT-H treatment, FBG level in T2DM mice was significantly lower than in the LUT group, indicating a superior hypoglycemic effect for PSLUT-H compared to LUT alone. However, FBG only captures a snapshot of blood glucose levels and can fluctuate, leading to potential misdiagnoses. Therefore, more accurate diagnostic tools are essential. Jiao et al. recommended OGTT, the AUC of OGTT, and serum GSP as additional indicators for diagnosing T2DM.²⁶ OGTT offers a more precise measure of pancreatic β-cell function and the body's ability to regulate glucose in T2DM mice. Additionally, calculating the AUC of OGTT provides a more comprehensive evaluation of glucose regulation, serving as a useful complement to OGTT measurements.

Serum GSP reflects blood glucose levels over a 2–3 weeks period and is not affected by short-term glucose fluctuations, making it a reliable indicator for assessing glucose metabolism and pancreatic β-cell function.²⁷ Consequently, FBG, OGTT, AUC of OGTT, and serum GSP together offer a more thorough assessment of T2DM risk. In this study, the OGTT curves for the LUT, PSLUT-L, and PSLUT-H groups positioned them between the Normal and Model groups. Additionally, the AUC of OGTT and serum GSP levels in the PSLUT-H group were significantly lower than in the LUT group, suggesting that PSLUT-H effectively alleviates hyperglycemia symptoms in T2DM mice compared to LUT alone. A well-accepted cause of T2DM is pancreatic β-cell dysfunction. Prolonged β-cell impairment can lead to insulin resistance, which is characterized by diminished sensitivity of insulin receptors in the liver, skeletal muscles, and other tissues, further exacerbating the progression of T2DM.

Assessing pancreatic β-cell function is vital in managing T2DM, as these cells are responsible for insulin secretion to maintain normal blood glucose levels.²⁸ The HOMA is commonly used for this purpose, with HOMA-β, HOMA-IR, and HOMA-IS reflecting β-cell function, insulin resistance, and insulin sensitivity, respectively. In this study, no notable differences were found between the PSLUT-L and LUT groups in terms of HOMA-β, HOMA-IR, and HOMA-IS, whereas the PSLUT-H group demonstrated significant improvements in HOMA-β and HOMA-IR compared to the LUT and PSLUT-L groups. This suggests that a higher dose of PSLUT enhances LUT bioavailability, aligning with the FBG and AUC of OGTT results.

T2DM not only causes hyperglycemia and β-cell dysfunction but also leads to lipid metabolism disorders and the release of inflammatory cytokines, aggravating inflammation in the liver and other tissues.²⁹ In this study, T2DM mice displayed lipid metabolic disturbances and tissue inflammation, with the PSLUT-H group showing significantly better outcomes in IL-6, IL-10, serum TC, serum TG, and liver HDL-c compared to the LUT group, highlighting superior regulation of lipid metabolism. Given the liver's key role in lipid regulation, an HSHF diet can induce liver damage, reducing the number and affinity of insulin receptors on hepatocyte membranes, which contributes to islet dysfunction and insulin resistance.³⁰

In this study, improvements in liver tissue were observed across the LUT, PSLUT-L, and PSLUT-H groups, with liver tissue scoring revealing significant recovery in the PSLUT-treated groups compared to the LUT group. The pathogenesis of T2DM involves multiple signaling pathways, and PSLUT alleviates hyperglycemia by regulating the transcription and protein expression of key targets such as AKT-1 and GLUT-2.³¹ Research shows that activated AKT-1 promotes GLUT-2 expression in liver tissue, enhancing glucose uptake, boosting energy metabolism, and increasing blood glucose utilization, thereby alleviating hyperglycemia. Therefore, studying the transcription and protein expression of AKT-1 and GLUT-2 in liver tissues is particularly relevant. The PSLUT-H group exhibited significantly higher transcription and protein expression levels of AKT-1 and GLUT-2 compared to the LUT group. Immunohistochemistry, which relies on the antigen–antibody specificity of protein detection, was used to locate and quantify specific protein expressions in cells.³² To further analyze these results, the H-Score was employed for semi-quantitative evaluation. The H-Score for AKT-1 and GLUT-2 in the PSLUT-L and PSLUT-H groups was significantly higher than in the LUT group, with the PSLUT-H group outperforming PSLUT-L. These results demonstrate that PSLUT-H substantially improved the expression of AKT-1 and GLUT-2 in the liver tissue of T2DM mice, thereby alleviating hyperglycemia.

The human gut hosts nearly 10 trillion bacteria, coexisting in a complex symbiotic relationship with the host. The balance of gut microbiota is critical for maintaining normal glucose metabolism.³³ In T2DM mice, Firmicutes, Bacteroidetes, and Proteobacteria dominate the intestinal microbiota, with Firmicutes and Bacteroidetes constituting over 90%. Numerous studies have established a close link between T2DM and an elevated Firmicutes/Bacteroidetes ratio.^20,34 In this study, treatment with LUT, PSLUT-L, and PSLUT-H significantly reduced the Firmicutes/Bacteroidetes ratio, consistent with findings by Liu et al.³⁵

At the genus level, Alistipes and Bifidobacterium, both beneficial bacteria, play critical roles in maintaining intestinal health. Walsh et al. reported that Alistipes supports intestinal homeostasis by producing metabolites such as fatty acids.³⁶Bifidobacterium enhances insulin sensitivity and alleviates hyperglycemia symptoms in T2DM by promoting SCFA production.³⁷ Conversely, Acetatifactor has been found to increase significantly in T2DM mice, and its presence is closely associated with intestinal inflammation.³⁸ Zhang et al. showed that LUT-containing bee pollen extract reshapes the gut microbiota in T2DM mice, significantly reducing the relative abundance of Candidatus-Arthromitus.³⁹ After 4 weeks of treatment, PSLUT-H markedly increased the relative abundance of Alistipes and Bifidobacterium in the intestines of T2DM mice, while significantly reducing the relative abundance of Acetatifactor and Candidatus-Arthromitus. It may be that PSLUT-H alleviates hyperglycemia symptoms by modulating the gut microbiota in T2DM mice, improving both glucose metabolism and inflammatory conditions.

As key metabolites produced by intestinal microbiota, SCFAs are generated through the fermentation of undigested carbohydrates and food residues by anaerobic bacteria in the gut.⁴⁰ SCFAs supply a substantial amount of energy to both intestinal microbiota and their host's intestinal epithelial cells, while also regulating the intestinal pH, promoting cell growth, and inhibiting opportunistic pathogens. From a disease prevention perspective, SCFAs contribute to reducing the incidence of metabolic disorders such as obesity, T2DM, and cardiovascular diseases by enhancing insulin sensitivity and maintaining glucose metabolism balance.⁴¹ Acetic acid, the most abundant SCFA, primarily affects adipose tissue, whereas propionic acid targets the colon and liver, and butyric acid acts on pancreatic tissue. This study demonstrated that after 4 weeks of intervention with LUT, PSLUT-L, and PSLUT-H, SCFA levels significantly increased in T2DM mice. Notably, isobutyric acid, butyric acid, isovaleric acid, valeric acid, and total SCFAs were significantly higher in the PSLUT-H group compared to the LUT group, indicating that PSLUT-H enhances these fatty acid levels in the cecum. Prior research has consistently highlighted the role of gut microbiota in regulating SCFA production. Gao et al. reported a significant positive correlation between Bifidobacterium and SCFAs such as acetic acid, propionic acid, and butyric acid, underscoring the beneficial impact on T2DM.⁴² Liu et al. also identified a positive correlation between Alistipes and acetic acid and butyric acid.⁴³ In contrast, Yang et al. found that Turicibacter, a serotonin-producing bacterium, did not significantly impact SCFA levels,⁴⁴ findings that align with the results of this study.

To explore the effects of PSLUT on hyperglycemia in T2DM mice, stratified cluster analysis was employed to examine the correlation between blood glucose indicators and bacterial genera. Bifidobacterium, known for its probiotic properties, has been shown to provide numerous health benefits when administered in appropriate amounts.⁴⁵ This research revealed that Bifidobacterium reduced AUC of OGTT, TG, FBG, and HOMA-IR levels by increasing acetic acid and other SCFAs, thereby improving hyperglycemia symptoms in T2DM.⁴⁵ These findings are consistent with this study, which demonstrated that PSLUT increased the relative abundance of Bifidobacterium and elevated SCFA levels, ultimately reducing liver inflammation and alleviating T2DM-induced hyperglycemia. Additionally, PSLUT improved glycemic markers such as FBG, serum GSP, AUC of OGTT, and HOMA-β by decreasing the relative abundance of Acetatifactor, Candidatus-Arthromitus, and other bacterial genera, thereby enhancing pancreatic β-cell function and glucose regulation in T2DM mice. Lachnoclostridium, Parasutterella, Turicibacter, and Papillibacter also exhibited strong correlations with blood glucose markers like FBG and AUC of OGTT, identifying them as potential gut biomarkers for T2DM research, in line with findings from Yuan et al.,⁴⁶ Guan et al.,⁴⁷ Song et al.,⁴⁸ and Wei et al.⁴⁹ In conclusion, compared to LUT alone, PSLUT showed superior stability in the upper digestive tract and improved bioavailability, resulting in more effective alleviation of hyperglycemia in T2DM mice. Notably, PSLUT-H demonstrated better hypoglycemic effects than PSLUT-L. This research reinforces the critical role of gut microbiota in the regulation of T2DM.

5 Conclusion

This study investigated the effects of PSLUT on hyperglycemia in T2DM mice, showing significant improvements across multiple hyperglycemia-related parameters, including body weight, FBG, AUC of OGTT, serum GSP, HOMA-IR, HOMA-β, and HOMA-IS. Notably, PSLUT-H outperformed PSLUT-L in regulating key indicators such as FBG, OGTT AUC, HOMA-β, HOMA-IR, serum TG, and liver HDL-c. Additionally, PSLUT treatment improved liver biochemical indicators and reduced liver tissue injury by modulating the relative transcription and protein expression levels of AKT-1 and GLUT-2. The study also highlighted PSLUT's ability to lower the Firmicutes/Bacteroidetes ratio and decrease the relative abundance of harmful bacterial genera, including Acetatifactor, Candidatus-Arthromitus, and Turicibacter. Moreover, Lachnoclostridium, Parasutterella, Turicibacter, and Papillibacter were strongly associated with hyperglycemia, suggesting their potential as biomarkers for studying T2DM progression. Overall, these findings provide a strong scientific foundation for further development of LUT-based interventions to mitigate hyperglycemia symptoms in T2DM.

Data availability

The data supporting this article have been included as part of the ESI.† Raw data of 16S rRNA sequence have been deposited at NCBI. Accession numbers are listed in the key resources table and the number for 16S rRNA sequence data reported in this paper is PRJNA1161355.

Conflicts of interest

The authors declare that the research was conducted without any commercial or financial relationships that could be construed as potential conflicts of interest.

Acknowledgements

This work was supported by the Major Basic Research Project of the Natural Science Foundation of the Jiangsu Higher Education Institutions (Grant No. 24KJA530004), the Yancheng Basic Research Plan Guiding Project (Grant No. YCBK2023051, YCBK2023076) and the School-Level Research Projects of the Yancheng Institute of Technology (Grant No. xjr2023046). The authors thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

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Footnotes

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4fo02907k

‡ Xiaodong Ge and Tingting Liu contributed equally to this study.

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