Use of np in the preparation of preparations for the regulation of intestinal metabolism in type 2 diabetes mellitus

Abstract

The application belongs to the technical field of biological medicine, and discloses application of NP in preparation of a preparation for regulating metabolism of the intestines of senile type 2 diabetes. The NP is formed by hydrogen bond combination of a peptide from soft-shelled turtle eggs with naringenin in a mass ratio of 100:1, and the mass of the peptide is less than 3 kDa; the preparation is used for regulating phenotype-related metabolic modules and intestinal micro-ecological-related metabolic modules, remodeling the intestinal metabolic regulation network taking differential metabolites as a bridge, and thus systemically improving metabolic disorders related to senile type 2 diabetes. It is found that NP intervention can obviously correct abnormal total metabolic profiles of feces of senile type 2 diabetes mice, and make the disease type metabolic profile shift to a new steady state of intervention type. Non-targeted metabolomics analysis by LC-MS / MS shows that the overall metabolic profile of the high-dose NP group is closer to that of the classic hypoglycemic drug metformin group, indicating that NP has a significant metabolic remodeling capacity.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to the application of NP in the preparation of intestinal metabolic regulators for type II diabetes mellitus caused by aging. Background Technology

[0002] Type 2 diabetes mellitus (T2) is a complex metabolic disease resulting from the combined effects of aging and glucose metabolism disorders. Its main characteristics include persistent hyperglycemia, insulin resistance, abnormal glucose and lipid metabolism, and multiple organ dysfunction. Compared to typical T2 diabetes, T2 in the aging context often involves increased oxidative stress, persistent chronic inflammation, impaired nutrient absorption and utilization, and behavioral abnormalities, making the disease's development and progression more complex and intervention more challenging. Therefore, developing novel intervention agents with multi-target regulatory effects for T2 has become an important direction in metabolic disease research.

[0003] In current technologies, interventions for type 2 diabetes in the elderly primarily rely on hypoglycemic drugs such as metformin, along with comprehensive measures including diet control and exercise management. While metformin has shown good efficacy in lowering blood glucose, improving insulin resistance, and regulating some metabolic abnormalities, its main focus remains on blood glucose regulation. Its ability to synergistically improve age-related complex metabolic network abnormalities, gut microbiota imbalances, and overall host phenotypic abnormalities remains limited. Especially under the dual effects of aging and diabetes, the body not only exhibits glucose metabolism disorders but also frequently experiences widespread disturbances in amino acid metabolism, nucleotide metabolism, lipid metabolism, vitamin and cofactor metabolism, bile acid metabolism, and substance transport processes. Interventions targeting a single point or along a single pathway often fail to restore overall metabolic homeostasis.

[0004] In recent years, with the development of metabolomics technology, an increasing number of studies have shown that gut metabolites play an important role in age-related metabolic diseases. Gut metabolites not only reflect the utilization and metabolic transformation of local nutrient substrates in the gut, but also serve as important functional mediators connecting the gut microbiota with the host's physiological and pathological phenotype, participating in multi-level processes such as glucose and lipid metabolism regulation, inflammatory responses, oxidative stress, organ damage, and behavioral changes. Particularly in age-related type 2 diabetes, the gut metabolic profile often undergoes significant remodeling, leading to abnormalities in aromatic amino acid metabolism, sulfur-containing amino acid metabolism, nucleotide turnover, lipid inflammatory mediator generation, bile secretion, and transmembrane transport, thus forming a continuous abnormal state of "gut microbiota—metabolic network—host phenotype." Therefore, seeking effective interventions from the perspective of gut metabolic network remodeling is of great significance for improving age-related metabolic disorders associated with type 2 diabetes.

[0005] In existing research, studies on bioactive peptides derived from soft-shelled turtle eggs largely focus on evaluating their in vitro enzyme inhibitory activity. For example, some studies have disclosed that soft-shelled turtle egg protein hydrolysates (molecular weight less than 2.5 kDa) possess α-glucosidase inhibitory activity and revealed their binding mode with the enzyme's active pocket through molecular docking. However, these studies only address in vitro biochemical activity and lack in vivo animal experiments for verification. Furthermore, they do not address the systemic regulatory effects of this peptide on the intestinal metabolic network, gut microbiota-related metabolic modules, and overall host phenotype in aging-related type 2 diabetes.

[0006] Current technologies for understanding the role of drugs or active substances in metabolite regulation largely focus on single metabolites or pathways, failing to fully elucidate their comprehensive regulatory effects on multi-level pathway networks, including amino acid metabolism, nucleotide metabolism, lipid metabolism, vitamin and cofactor metabolism, bile secretion, protein digestion and absorption, and substance transport. In particular, there is a lack of systematic research on how different interventions can reshape metabolic pathways, adjust key metabolic nodes, and improve the overall metabolic phenotype of the host.

[0007] In view of this, the present invention is proposed. Summary of the Invention

[0008] The present invention aims to solve at least one of the above technical problems and provides the application of NP in the preparation of intestinal metabolic regulation agents for type 2 diabetes mellitus of the aging process. The agents can effectively regulate the abnormal metabolites associated with type 2 diabetes mellitus of the aging process, reshape the intestinal metabolic profile and improve the host-related pathological phenotype.

[0009] Compared with the prior art, the present invention has the following beneficial effects: This invention reveals that NP intervention can significantly correct abnormal fecal total metabolic profiles in aging type 2 diabetic mice, shifting the disease-related metabolic profile towards a new intervention-induced homeostasis. LC-MS / MS non-targeted metabolomics analysis showed that the overall metabolic profile of the high-dose NP group was closer to that of the classic hypoglycemic drug metformin group, suggesting that NP has a significant metabolic remodeling capacity. Furthermore, it was found that NP can simultaneously act on multiple functional pathways, including amino acid metabolism, nucleotide metabolism, lipid metabolism, bile secretion, protein digestion and absorption, ABC transport, and inflammatory oxidative stress, demonstrating a networked overall regulatory advantage rather than local correction of a single target.

[0010] The NP provided by this invention not only improves intergroup differential metabolite abnormalities but also modulates characteristic metabolic modules (MEgreen / MEblue phenotypic feature modules, containing 632 hub metabolites) associated with blood glucose, insulin resistance, inflammation, oxidative stress, liver damage, and behavioral abnormalities, suggesting that its intervention effect is directly related to the improvement of multidimensional host phenotypes. The NP can also modulate metabolic modules highly correlated with changes in gut microbiota (MEbrown / MEpink gut microbiota feature modules, containing 483 hub metabolites), suggesting that it exerts its intervention effect through a continuous axis of action of "microbiota-metabolic network-host phenotype".

[0011] The NP provided by this invention can affect pathways such as arachidonic acid metabolism, linoleic acid metabolism, glutathione metabolism, and ferroptosis, which is beneficial for alleviating chronic inflammation, oxidative stress, and membrane lipid damage. It can also regulate some neuroactive small molecule-related metabolic pathways (such as neuroactive ligand-receptor interactions, serotonergic synapses, etc.), and has the potential to improve age-related behavioral abnormalities associated with type 2 diabetes. Attached Figure Description

[0012] Figure 1 (A) shows the particle size, potential and PDI value of the NP complex in different proportions in Example 1; (B) shows the UV full-wavelength scan of NP and turtle egg peptide in different proportions in Example 1; (C) shows the fluorescence spectrum of NP and turtle egg peptide in different proportions in Example 1; and (D) shows the secondary structure analysis of the complex NP, naringenin and turtle egg peptide by Fourier transform infrared spectroscopy in Example 1. Figure 2 The image shows the microstructure of the turtle egg-derived peptide, naringenin, and NP complex in Example 1 under a laser confocal microscope. Figure 3 The analysis of mouse fecal metabolites in Example 4 includes (A) PCA score map under POS ion mode, (B) PCA score map under NEG ion mode, (C) PLS-DA score map, (D) PLS-DA substitution test, (E) Veen plot, and (F) HMDB compound classification. Figure 4 The analysis of mouse fecal metabolites in Example 4 shows that (A) is a compound classification and distribution map, (B) is a KEGG pathway statistical map, and (C) is a KEGG pathway statistical histogram. Figure 5 The top 20 KEGG pathways in mouse fecal metabolites in Example 4; Figure 6 A heatmap showing the correlation between metabolites, biochemical indicators, and behavioral patterns in Example 9; Figure 7 This is a correlation analysis diagram between modules and phenotypes in Example 9; Figure 8 This is a cluster analysis diagram of metabolites and phenotypes in Example 9; Figure 9 (A) is the VIP diagram of the phenotypic metabolites in Example 10, and (B) is the KEGG pathway secondary classification diagram of the phenotypic metabolites in Example 10. Figure 10 This is a three-level classification diagram of the KEGG pathway for phenotypic metabolites in Example 10; Figure 11 This is a network diagram of KEGG pathway enrichment analysis in Example 11; Figure 12 (A) is a bubble chart of KEGG enrichment analysis in Example 11, and (B) is a bubble chart of KEGG topology analysis in Example 11. Figure 13 This is a heatmap showing the correlation between metabolites and gut microbiota in Example 12; Figure 14 This is a graph showing the correlation between the modules in Example 12 and the gut microbiota. Figure 15 This is a cluster analysis diagram of metabolites and gut microbiota in Example 12; Figure 16 (A) is the VIP diagram of the gut microbiota characteristic metabolites in Example 13, and (B) is the KEGG pathway secondary classification diagram of the gut microbiota characteristic metabolites in Example 13; Figure 17 This is a three-level classification diagram of the KEGG pathway for characteristic metabolites of the gut microbiota in Example 13; Figure 18 (A) Example 14 shows the KEGG pathway enrichment analysis network diagram of gut microbiota-related metabolites, and (B) shows the KEGG enrichment analysis bubble diagram of gut microbiota-related metabolites in Example 14. Figure 19 This is a bubble diagram of KEGG topology analysis of gut microbiota-related metabolites in Example 14; Figure 20 KEGG regulatory network analysis of differential metabolites and phenotypic characteristic metabolites in Example 15; Figure 21 This study analyzes the KEGG regulatory network of differential metabolites and gut microbiota characteristic metabolites in Example 15. Detailed Implementation

[0013] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] This invention provides the application of NP in the preparation of an intestinal metabolic regulation agent for type 2 diabetes mellitus of the elderly. The NP is a nanoscale complex formed by hydrogen bonding of turtle egg peptide with a molecular weight of less than 3 kDa and naringenin at a mass ratio of 100:1. The preparation reshapes the intestinal metabolic regulation network with differential metabolites as a bridge by regulating phenotype-related metabolic modules and intestinal microecology-related metabolic modules, thereby systematically improving age-related type 2 diabetes mellitus-related metabolic disorders.

[0015] The turtle egg-derived peptides are peptide components with a molecular weight of less than 3 kDa obtained by centrifugation, filtration, and ultrafiltration after papain enzymatic hydrolysis of freeze-dried turtle egg powder. The preparation method of this peptide mixture can refer to the method disclosed in the prior art CN120081895A, which identifies and screens multiple representative active peptide sequences (such as YD, VR, DA, RVR and SEQ ID NO:1~12) from similar peptide mixtures. However, this application is not limited to the independent action of a single peptide sequence, but utilizes the NP complex formed by the overall peptide mixture with a molecular weight of less than 3 kDa and naringenin to achieve overall regulation of the intestinal metabolic network.

[0016] The turtle egg-derived peptides were prepared using conventional biological separation methods such as papain enzymatic hydrolysis, centrifugation, filtration, and ultrafiltration. These methods are merely exemplary, and those skilled in the art can also use other equivalent enzymatic hydrolysis and separation purification processes to obtain turtle egg-derived peptide components with a molecular weight of less than 3 kDa.

[0017] Naringenin, a common flavonoid compound in citrus fruits, possesses antioxidant and anti-inflammatory biological activities. However, it suffers from technical bottlenecks such as low bioavailability and poor water solubility, and there are few applications for direct consumption of naringenin. Combining the aforementioned turtle egg-derived peptide with naringenin at a mass ratio of 100:1 allows for the formation of a stable nanoscale complex through hydrogen bonding, thereby improving the dispersibility and bioavailability of naringenin and producing systemic metabolic regulatory effects that cannot be expected from a single component.

[0018] For phenotype-related metabolic modules and gut microbiota-related metabolic modules, weighted gene co-expression network analysis (WGCNA) was used to cluster metabolites with the same expression patterns into functional modules. Modules significantly correlated with host biochemical and behavioral phenotypes were identified as phenotype-related metabolic modules; modules highly correlated with changes in gut microbiota were identified as gut microbiota-related metabolic modules. These modules are not simply classifications of metabolites, but rather biological units reflecting synergistic metabolic functions.

[0019] The phenotype-associated metabolic module includes the MEgreen and MEblue modules, containing a total of 632 hub metabolites. The metabolic pathways involved in this module primarily include protein digestion and absorption, amino acid metabolism, ABC transport, nucleotide metabolism, bile secretion, and sphingolipid signaling. These pathways reflect the utilization of nitrogenous substrates in the gut and the co-metabolic state of amino acids between the host and the gut microbiota, while also involving membrane lipid homeostasis, inflammatory lipid mediator signaling, and transmembrane transport processes.

[0020] The gut microbiota-related metabolic module includes the MEbrown and MEpink modules, containing a total of 483 hub metabolites. The metabolic pathways involved in this module primarily include linoleic acid metabolism, arachidonic acid metabolism, glutathione metabolism, ferroptosis, cysteine ​​and methionine metabolism, tryptophan metabolism, protein digestion and absorption, neuroactive ligand-receptor interactions, and serotonergic synapses. These pathways mainly involve unsaturated fatty acid metabolism, lipid inflammatory mediator generation, membrane lipid oxidative damage, redox homeostasis imbalance, and gut-brain axis-related metabolic changes.

[0021] It should be noted that the WGCNA analysis divided total metabolites into 9 modules (MEgreen, MEblack, MEblue, MEturquoise, MEpink, MEred, MEbrown, MEyellow, MEgrey). MEgreen / MEblue was identified as a phenotype-related metabolic module due to its significant association with phenotype, and MEbrown / MEpink was identified as a gut microbiota-related metabolic module due to its significant association with gut microbiota. The remaining modules did not show a significant association with phenotype or gut microbiota.

[0022] Differential metabolites from multiple groups act as a bridge between phenotype-associated metabolic modules (PAMs) and gut microbiota-associated metabolic modules (GEMs). Specifically, the GEMs, as the upstream driving layer, transmit changes to the PAMs (downstream output layer) through differential metabolites, ultimately resulting in improvements in the host phenotype. This hierarchical relationship of "upstream microbiota-driven—differential metabolite bridging—downstream phenotypic output" constitutes the core mechanism by which NP systematically improves metabolic disorders.

[0023] The remodeling of the intestinal metabolic regulatory network includes a first regulatory network between differential metabolites and phenotype-related metabolic modules, and a second regulatory network between differential metabolites and intestinal microecology-related metabolic modules.

[0024] The first regulatory network is a complex network in which differentially expressed metabolites and phenotype-associated metabolites are co-embedded. It consists of multiple KEGG pathway nodes, enzyme nodes, reaction nodes, and compound nodes, mainly including pathway nodes such as map00480, map04115, map00561, map04961, map04142, map00511, map05219, map01232, map00780, map05231, map04211, and map04612. This network is interconnected through multiple shared reactions and intermediate substrates, suggesting a significant functional synergistic relationship between differentially expressed metabolites and phenotype-specific metabolites. Phenotypic abnormalities such as host blood glucose, inflammation, oxidative stress, and behavioral impairment are a complex regulatory network composed of differentially expressed metabolites, key enzyme reactions, and phenotype-associated metabolites.

[0025] The second regulatory network mainly involves backgrounds such as glycolipid complex metabolism, sphingolipid and membrane lipid pathways, fat digestion and absorption, protein digestion and absorption, ferroptosis, and sulfur-containing metabolism. This network exhibits more pronounced clustering and functional modularity, suggesting that gut microbiota-specific metabolites are more likely to represent functional metabolic units driven by changes in the gut microbiota, with stronger upstream constraints and more concentrated functional attributions.

[0026] The turtle egg-derived peptide component and naringenin were mixed and dissolved at a mass ratio of 100:1, forming the nanoscale complex through hydrogen bonding. This mixing and dissolution method is merely exemplary; any method that achieves sufficient contact between the two components at a mass ratio of 100:1 and the formation of a stable complex through hydrogen bonding is acceptable. Fourier transform infrared spectroscopy analysis confirmed that the NH stretching vibration peak in the peptide chain and the phenolic hydroxyl stretching vibration peak in naringenin shifted after complexation, and the amide I band peak also shifted, indicating that hydrogen bonding led to a change in the secondary structure of the peptide. Laser confocal microscopy revealed that the NP exhibited a nanoscale morphology. Particle size analysis showed that the optimal complex particle size was approximately 453 nm, the zeta potential was approximately -21.53 mV, and the PDI was approximately 0.49.

[0027] The formulations of this invention are administered orally, with an effective dose of 0.5-1 g / kg·bw. The routes of administration and dosage ranges described are merely exemplary. Those skilled in the art can adaptively adjust the dosage and intervention period according to the specific dosage form of the formulation, individual differences in the test subjects, and treatment goals, as long as the formulation contains an effective dose of the NP complex.

[0028] The formulation may be an oral solid dosage form (such as tablets, capsules, granules) or a liquid dosage form (such as oral liquid, suspension). The selection of the dosage form and the preparation method are conventional techniques in the field, and the specific excipients and processes can be determined based on conventional knowledge of pharmacology. The above dosage form limitations are merely illustrative.

[0029] By reshaping the aforementioned intestinal metabolic regulatory network, NP can simultaneously improve metabolic disorders associated with blood glucose, insulin resistance, inflammation, oxidative stress, liver function impairment, and behavioral phenotypes. This simultaneous improvement is not a local correction targeting a single point, but rather achieved through the overall reshaping of a multi-level metabolic pathway network. High-dose NP exhibits stronger metabolic profile correction capabilities and broader coverage of differential metabolic pathways, and its metabolic regulatory direction shows some consistency with the classic hypoglycemic drug metformin, while retaining characteristic metabolic regulatory patterns.

[0030] The technical solution of this application is further illustrated below through specific embodiments. It should be noted that the following embodiments are for illustrative purposes only and do not constitute a limitation on the scope of protection of this application.

[0031] Example 1: Preparation and Characterization of NP The solution of lyophilized turtle egg powder (concentration 5%) was prepared by enzymatic hydrolysis with papain (5000 U / g) at 50 °C for 4 h, centrifuged at 10000 g at 4 °C for 20 min, filtered through 4 layers of 200 mesh gauze, filtered through a 0.45 μm filter membrane, and then ultrafiltered through 5 kDa and 3 kDa ultrafiltration plates to obtain turtle egg-derived peptides (molecular weight less than 3 kDa). After vacuum freeze-drying, the peptides were stored at -80 °C for later use. Turtle egg peptides were dissolved in ultrapure water to obtain turtle egg peptide solutions (20 mg / mL, 50 mg / mL, 100 mg / mL, 125 mg / mL and 150 mg / mL), while naringenin was dissolved in 75% ethanol to obtain naringenin solution (1 mg / mL). The naringenin solution prepared above was slowly added dropwise to the turtle egg peptide solution at a ratio of 1:1 (v / v), and stirred at room temperature for 2 hours to obtain NP complexes of turtle egg peptide and naringenin in different ratios (20:1, 50:1, 100:1, 125:1 and 150:1, w / w).

[0032] The particle size, potential, and PDI of NP were determined using a Malvern Master Sizer 3000 particle size analyzer. The refractive index of water was set to 1.33, and the experimental temperature was set to 25°C. The results are as follows: Figure 1As shown in (A), when the mass ratio of turtle egg peptide to naringenin is 100:1, the particle size of the composite system is the smallest, which is 452.97 nm. At this time, the corresponding ζ potential value is the largest and the PDI value is the smallest, which are -21.53 mV and 0.49, respectively. This indicates that the NP complex system prepared at this ratio is the most stable. In subsequent Examples 2-15, NP with a mass ratio of 100:1 was selected.

[0033] The complexes of turtle egg-derived peptides and naringenin at different ratios (20:1, 50:1, 100:1, 125:1, and 150:1, w / w) and the turtle egg-derived peptides were subjected to UV full-wavelength scanning. Results are as follows: Figure 1 As shown in (B), all substances exhibit an absorption peak at 275 nm. Except for the turtle egg peptide (Pep), the complex NP with different proportions all show double peaks at 275 nm and 323 nm. The absorption peak of the peptide itself is the weakest. As the peptide content decreases, its ultraviolet absorption peak gradually increases, indicating that naringenin and turtle egg peptide interact to form the NP complex, which has the common characteristic absorption peaks of peptide and naringenin.

[0034] Fluorescence spectroscopy analysis was performed on the NP complexes of turtle egg-derived peptides and naringenin at different ratios (20:1, 50:1, 100:1, 125:1, and 150:1, w / w) and the turtle egg-derived peptides. The results are as follows: Figure 1 As shown in (C), the peptide itself exhibits strong fluorescence emission under 260 nm excitation. When the turtle egg-derived peptide forms a complex with naringenin, the fluorescence intensity of all complexes is significantly lower than that of the turtle egg-derived peptide, indicating that naringenin has a "quenching effect" on the peptide's fluorescence after binding with the peptide. As the proportion of naringenin gradually decreases, the fluorescence intensity gradually increases, indicating that naringenin can quench the peptide's fluorescence. The degree of quenching is positively correlated with the relative content of naringenin. However, no peak shift was observed in the figure, indicating that the binding of naringenin with the turtle egg-derived peptide did not change the center wavelength of the peptide's fluorescence emission, but only reduced its fluorescence intensity.

[0035] A 100:1 mass ratio NP solution was concentrated using a high-throughput vacuum parallel concentrator at 37°C and 170 rpm. The concentrate was then freeze-dried under vacuum to obtain the lyophilized NP complex. FTIR peak shifts and intensity changes revealed an interaction between the turtle egg peptide and naringenin, forming the NP complex. Results are as follows: Figure 1 As shown in (D), in the range of 3200~3400cm -1 The region mainly consists of the stretching vibration region of hydroxyl groups; naringenin (Nar) is located at 3290 cm⁻¹. -1 The presence of OH⁻ stretching vibrations at this location exhibits the phenolic hydroxyl characteristic of flavonoids; the turtle egg-derived peptide (Pep) shows a vibration at 3294 cm⁻¹. -1The peak at the 1000 cm⁻¹ is mainly due to the stretching vibration of NH in the peptide chain; the peak of NP shifts to 3286 cm⁻¹. -1 The peak is wider at this location, which may be due to hydrogen bonding between the hydroxyl and amino groups of the turtle egg peptide and naringenin, indicating that this may be the main force binding the peptide and naringenin. Furthermore, in the protein's secondary structure characteristic region of 1600–1700 cm⁻¹... -1 At this location, Pep is 1647cm. -1 There is an amide I peak at this point, and the NP peak shifts to 1643 cm⁻¹. -1 At this location, naringenin showed no obvious characteristic peak. This may be because the secondary structure of the peptide changed after the peptide and naringenin bound together, which is a manifestation of the change in spatial conformation after the peptide and naringenin bind together.

[0036] Laser confocal microscopy revealed the interaction between turtle egg-derived peptides (Peptide) and naringenin, forming NPs. For example... Figure 2 As shown, the NP complexes have different sizes and shapes, and the resulting NPs are nanoscale complexes, which is a direct manifestation of the interaction between the two.

[0037] Example 2: Construction of an aging type 2 diabetic mouse model 8-week-old male C57BL / 6 mice (ethical review number: GOU-LAE-2025-013) were purchased from Zhuhai Bestong Biotechnology Co., Ltd., weighing 18.0 - 20.0 g (license number: SCXK (Guangdong) 2020-0051). The mice were housed at a temperature of 22 ± 2 °C and a humidity of 50 ± 10%, with a 12-h light / dark cycle, and the adaptation period was 1 week; after the adaptation period ended, the mice were randomly divided into 5 groups, with 12 mice in each group, a total of 60 mice. The first group was the blank control group, and the remaining 4 groups were the aging model groups, which were intraperitoneally injected with 500 mg / kg·bw D-galactose at 9:00 am every day; the blank control group was intraperitoneally injected with an equal amount of normal saline, and the injection was continued for 9 weeks. At the same time, during the 6th week, the mice were fasted for 12 h without water restriction; the mice in the aging model groups were intraperitoneally injected with 70 mg / kg·bw streptozotocin (STZ) for 5 consecutive days. STZ was dissolved in 0.1 M citric acid buffer (pH 4.5) and prepared freshly before use. The whole operation was carried out in the dark; the blank control group was injected with an equal amount of sterile citric acid buffer, and a 20% glucose aqueous solution was given 4 h after the injection to prevent the death of the modeled animals due to too low blood glucose; 5 days after the injection, after the mice were fasted for 12 h without water restriction, the fasting blood glucose (FBG) of all mice was measured with a portable blood glucose meter. Mice with FBG > 11.1 mmol / L were classified as aging type 2 diabetic mice; the remaining mice with unqualified blood glucose values were intraperitoneally injected with STZ again, and after the blood glucose reached the standard, they were also classified as aging type 2 diabetic mice, and the unqualified mice were excluded (4 mice). The mice in the blank control group were fed a basal maintenance diet throughout the experiment, while the mice in the aging model groups were fed a high-fat diet, and the mice could eat and drink freely. During the establishment of the diabetic model, 2 mice died accidentally.

[0038] The aging type 2 diabetic mice (n = 54) were randomly divided into 4 groups, and the specific grouping and treatment were as follows: (1) Young healthy control group (Control, n = 12): gavaged with an equal amount of normal saline; (2) Aging type 2 diabetic model group (Model, n = 11): gavaged with an equal amount of normal saline; (3) Metformin positive drug group (Met, n = 10): gavaged with metformin, 150 mg / kg·bw; (4) Low-dose NP group (NPL, n = 10): gavaged with NP, 0.5 g / kg·bw; (5) High-dose NP group (NPH, n = 11): gavaged with NP, 1 g / kg·bw; NP was intervened for 9 weeks, and an animal behavior experiment was carried out in the 16th week. The above injection and gavage doses were 0.1 mL / 10g·bw, and the mice were sacrificed in the 18th week for the detection of relevant physiological, biochemical and histological indexes.

[0039] Example 3: Non-targeted metabolomics detection in mouse feces Weigh approximately 20±5 mg of mouse fecal sample into a 2 mL centrifuge tube, add 6 mm grinding beads and 400 μL of pre-cooled methanol-water extraction buffer (4:1, v / v), which contains internal standard L-2-chlorophenylalanine, etc. Grind the sample at -10 ℃ and 50 Hz for 6 min, then sonicate at 5 ℃ and 40 kHz for 10 min, and place at -20 ℃ for 30 min. Then centrifuge at 4 ℃ and 13000 g for 15 min, collect the supernatant and transfer it to a sample vial for LC-MS analysis.

[0040] A separate 20 μL supernatant was taken from each sample and mixed in equal volumes to prepare a QC sample. Chromatographic separation was performed using a Thermo Scientific Vanquish Horizon UHPLC system equipped with an ACQUITYUPLC HSS T3 column (100 mm × 2.1 mm, 1.8 μm). Mobile phase A was 95% water / 5% acetonitrile solution (containing 0.1% formic acid), and mobile phase B was 47.5% acetonitrile / 47.5% isopropanol / 5% aqueous solution (containing 0.1% formic acid); the injection volume was set to 3 μL, and the column temperature was maintained at 40 ℃. Mass spectrometry detection was performed using a Q-Exactive HF-X mass spectrometer, with data acquired in ESI positive and negative ion modes. The relevant parameters were set as follows: scan range m / z 70–1050, sheath gas flow rate 50 arb, auxiliary gas flow rate 13 arb, heating temperature 425 ℃, capillary temperature 325 ℃, spray voltage set to +3500 V and -3500 V respectively, S-Lens RF Level 50, normalized collision energy set to 20%, 40% and 60%, Full MS and MS 2 The resolutions were 60,000 and 7,500, respectively. Raw data were processed using Progenesis QI v3.0 software, including peak identification, integration, retention time correction, and peak alignment, to generate a data matrix. This data was then combined with MS / MS information and annotated with reference to HMDB, METLIN, and Shanghai Meiji Biotechnology's self-built database, with quality errors controlled within 10 ppm. QC samples were prepared by mixing all sample extracts in equal volumes. One QC sample was inserted every 5–15 samples during the detection process to assess system stability. Internal standard z-score analysis was also used to implement quality control throughout the entire detection process.

[0041] Example 4: Analysis of mouse fecal metabolite profiles Mouse fecal metabolites were analyzed using LC-MS / MS combined with POS and NEG modes. The results are as follows: Figure 3As shown. In POS mode, 6912 ion peaks were obtained, of which 1480 metabolites were identified, and 5432 substances remained unidentified; in NEG mode, 10250 ion peaks were obtained, with 1457 metabolites identified, and 8793 substances remained unidentified. According to... Figure 3 (A) and Figure 3 (B) It can be seen that all samples are within the 95% confidence level, and the QC points are well clustered, indicating that the instrument is operating stably, the data repeatability is good, and the sample pretreatment and detection procedures are reliable. The control group is significantly separated from other groups, suggesting that the fecal metabolic environment of the young healthy group is fundamentally different from that of the aging type 2 diabetes and various intervention groups, and that aging type 2 diabetes significantly reshapes the intestinal metabolic ecology. The model overlaps with the intervention group, but not completely, suggesting that although the intervention changed the metabolic profile, the change was limited and not enough to fully restore the control state. The clustering relationship between NPH and Met is closer, while that of NPL is more dispersed, suggesting that the metabolic remodeling direction of high-dose NP is closer to the intervention direction of classic hypoglycemic drugs.

[0042] In the PLS-DA permutation test, the Q² intercept is negative, indicating that the original model R0 2 Q 2 The results are clearly separated from those of random permutation, indicating that the PLS-DA model has certain explanatory and predictive power, and that the model is stable and not overfitting. Figure 3 (D)). In the PLS-DA score chart ( Figure 3 In (C), the Control group remained significantly separated from other groups. The Model group showed some separation from Met and NPH, but was still separated from the Control group, suggesting that Met and NP could correct model-related metabolic abnormalities to varying degrees. The overall metabolic profile of the high-dose NP group was closer to that of the metformin group, while the low-dose NP group was between the model and the high-dose intervention, suggesting that the metabolic regulatory effect of NP is dose-dependent. However, neither metformin nor NPH completely restored the intestinal metabolic profile of aging type 2 diabetic mice to the level of young healthy mice. Venn figure ( Figure 3 (E) showed a large number of common metabolites among the five groups of mice, with many shared central components, while group-specific metabolites were relatively few, suggesting that the differences between groups mainly reflected in relative abundance rearrangement. Annotation and classification of the differentially expressed metabolites using the Human Metabolome Database (HMDB) revealed that these metabolites were mainly distributed in categories such as carboxylic acids and their derivatives, and fatty acyl groups. Figure 3 (F) and Figure 4 (A)). The overall composition of different groups at the HMDB category level is quite similar, indicating that the metabolic types most significantly affected in this invention are mainly concentrated in amino acid / organic acid metabolism and lipid metabolism. Further pathway annotation results show that ( Figure 4(B) The pathways of tryptophan metabolism, tyrosine metabolism, phenylalanine metabolism, purine metabolism, and glycerophospholipid metabolism were all covered by a large number of differentially metabolized substances, suggesting that the aging-related type 2 diabetes state may cause disorders in aromatic amino acid metabolism, nucleotide turnover, and lipid metabolism by disturbing the co-metabolic network between the gut microbiota and the host. After high-dose NP intervention, the above abnormalities can be corrected to some extent, but have not yet fully recovered to the level of youthful health.

[0043] KEGG histogram results further indicate that ( Figure 4 (C) ), most of the differentially metabolites belong to the metabolism-related category, suggesting that fecal metabolic abnormalities are mainly concentrated at the metabolic pathway level, especially involving functional modules such as amino acid metabolism, nucleotide metabolism, lipid metabolism, and nutrient transport and absorption. Among the top 20 KEGG pathways ( Figure 5 The pathways involved in tryptophan metabolism, purine / pyrimidine metabolism, phenylalanine-tyrosine-tryptophan biosynthesis, glycerophospholipid metabolism, linoleic acid metabolism, amino acid biosynthesis, and ABC transport were all enriched with a significant number of differentially metabolites. This indicates that aging-related type 2 diabetes not only alters the composition of local intestinal metabolites but may also further reshape the host-microbiota co-metabolism network, nutrient substrate utilization, and transmembrane transport processes. Among these, the aromatic amino acid metabolism-related pathways showed a persistently high enrichment, suggesting that they may be important nodes connecting intestinal microbiota imbalance, increased inflammation and oxidative stress, and abnormal neuro-metabolic regulation. Meanwhile, abnormalities in purine / pyrimidine metabolism and glycerophospholipid metabolism also suggest that nucleotide turnover, energy stress, and membrane lipid homeostasis imbalance are also involved in this pathological process. Combined with the aforementioned PCA / PLS-DA analysis results, it is evident that both Met and high-dose NP intervention can reshape these metabolic pathways to some extent, but have not yet fully restored them to the levels of the young, healthy group, suggesting that their effects are more likely to manifest as partial correction and reconstruction of new homeostasis in the intestinal metabolic imbalance associated with aging-related type 2 diabetes.

[0044] Example 5: Comparative analysis of metabolites between the two groups After determining that different interventions remodeled the fecal metabolic profile of mice, volcano plots were used to further understand the main metabolite types altered between the two groups in the aforementioned pathways. The results showed that the Model group exhibited the most significant metabolite differences compared to the Control group, with 725 metabolites downregulated and 398 upregulated. Indole, some amino acids and glycosides, and lipid or carboxylic acid derivatives showed the largest changes, suggesting that the aging type 2 diabetes model significantly altered a large number of metabolites in mouse feces and significantly changed the intestinal metabolic microenvironment. Compared to the Model group, the Met group showed 99 upregulated and 150 downregulated metabolites; the NPL group showed 23 upregulated and 51 downregulated metabolites; and the NPH group showed 159 upregulated and 191 downregulated metabolites. This suggests that metformin and NP have significant regulatory effects on model-related metabolic imbalances, with NP intervention showing a dose-dependent effect. Compared to the Control group, the Met group upregulated 368 metabolites and downregulated 766 metabolites, the NPL group upregulated 351 metabolites and downregulated 812 metabolites, and the NPH group upregulated 399 metabolites and downregulated 814 metabolites. This suggests that NP and metformin are consistent in regulating fecal metabolic imbalance in aging type 2 diabetic mice, but some characteristic metabolites of each group show significant differences. Overall, metformin and NP interventions still show some differences compared to the metabolites in the young healthy group. Compared to the Met group, the NPL group upregulated 134 metabolites and downregulated 263 metabolites, and the NPH group upregulated 187 metabolites and downregulated 191 metabolites. This suggests that NP and metformin have different effects on the regulation of fecal metabolites in aging type 2 diabetic mice. High-dose NP is closer to Met, but still has its own characteristic metabolites. Compared to NPL, the NPH group upregulated 476 metabolites and downregulated 211 metabolites, suggesting that NP intervention has a clear dose-dependent effect, and high and low doses may act through different metabolic regulatory pathways.

[0045] Further observation of differentially metabolites between the two groups revealed that indole, L-homocysteine, putrescine, hydroxyputrescine, 8-epideoxystrychic acid, and some drug-related small molecules (metformin and cyanoguanidine) repeatedly appeared in multiple comparison groups, suggesting that aromatic amino acid metabolism, polyamine metabolism, and sulfur-containing amino acid metabolism may be important differentially metabolites between the two groups. Combined with the aforementioned KEGG analysis results of total metabolites, this indicates that aging-related type 2 diabetes may cause abnormalities in tryptophan / tyrosine / phenylalanine metabolism, nucleotide metabolism, and lipid metabolism by remodeling the gut microbiota and host co-metabolic network; metformin and high-dose NP can, to some extent, regulate these imbalances by constructing new metabolic homeostasis.

[0046] Example 6: Characteristic metabolic fingerprint analysis between two groups To further observe the correlation characteristics of metabolites among groups at a holistic level, characteristic metabolic fingerprint analysis was performed on differentially expressed metabolites between the two groups. The results showed that the Model and Control groups had the most differentially expressed metabolites and the largest amplitude, indicating that the combined effects of aging and diabetes on the mouse fecal metabolome were not localized, but rather formed the most significant and widespread metabolic fingerprint shift. This suggests that a significant remodeling of the gut microbiota and host co-metabolic network occurred in the aging-related type 2 diabetes state. NPL showed the least difference compared to the Model group, while NPH showed a significantly increased difference. Met fell between NPL and NPH, suggesting that both Met and NP could improve the shift in mouse fecal metabolic fingerprint caused by aging and diabetes. The regulation by NP was dose-dependent, with a weak effect at low doses and a significant effect at high doses. Further analysis revealed 1123 differentially expressed metabolites in the Model group compared to the Control group, 900 of which were unique to this comparison, suggesting that aging-related type 2 diabetes can induce large-scale and specific intestinal metabolic imbalances. In contrast, compared to the Model group, Met, NPL, and NPH had 249, 74, and 350 differentially expressed metabolites, respectively, indicating that the interventions selectively, rather than comprehensively, improved the abnormal metabolic profile of the model. Notably, the NPH group had a significantly higher number of differentially expressed metabolites than the NPL group, and shared some of the differentially expressed metabolite sets with the metformin group, suggesting that the metabolic regulatory effect of NP is not only dose-dependent, but that NPH's reconstruction of the overall metabolites is consistent with that of metformin.

[0047] Example 7: Analysis of metabolite clustering, VIP, correlation, and KEGG functional pathways among multiple groups. Metabolic sets were created from differentially expressed metabolites across multiple groups for comprehensive analysis. Based on these sets, hierarchical clustering was performed on the samples and the top 50 metabolites. The results showed that the Control, Model, Met, NPL, and NPH groups exhibited a clear stratification trend in the overall heatmap, rather than being completely random and mixed. This indicates that the selected differentially expressed metabolites have strong inter-group discriminative ability. Some metabolites showed relatively consistent high or low abundance patterns in the Model group, a clear opposite to the high-low expression pattern in the Control group. Furthermore, each differentially expressed metabolite was divided into multiple subclusters, and the direction of change in these subclusters was not entirely consistent across groups. In the Met and NPH groups, reverse shifts or partial reversions were observed. The NPL group was closer to the Model group, suggesting that the model group forms an aging-related type 2 diabetes metabolite profile involving multiple metabolic axes. NP and Met have regulatory and reconstructive effects on this disease's metabolite profile, with NPL showing limited intervention strength and NPH exhibiting a more significant regulatory effect. Analysis of the top 30 differentially expressed metabolites in multiple groups revealed that 8-epideoxystrychnine had the highest VIP value, followed by Asn-Asn-Asn, aucubin, L-homocysteine, Tyr-Phe-Lys, Ala-Lys-Ser, and butirocin. This suggests that these metabolites may be key candidate biomarkers for differentiating disease states and intervention effects.

[0048] Correlation network diagrams were used to establish connections between differentially metabolites across multiple groups. Overall, the correlation network was extremely dense, indicating extensive structural correlations among the differentially metabolites. Some metabolites showed unidirectional changes, while others suggested potential substrate competition, substitution, or opposite metabolic regulation between different metabolic modules. The chemical categories involved included fatty acyl groups, organic sulfuric acids and their derivatives, organic oxygen compounds, isopentenol lipids / polyisoprene lipids, carboxylic acids and their derivatives, benzopyrans, and imidazopyrimidines. This is consistent with the previous HMDB classification and KEGG results, suggesting that differentially metabolites across multiple groups form a complex network involving amino acid metabolism, lipid metabolism, oxidative stress-related metabolism, and gut microbiota co-metabolism.

[0049] Further analysis of the pathways of differentially metabolized metabolites revealed that the vast majority of them fell into the major metabolic category, suggesting that aging-related type 2 diabetes and Met and NP interventions alter the metabolic function of mice. This category mainly includes amino acid metabolism, lipid metabolism, cofactor and vitamin metabolism, nucleotide metabolism, and carbohydrate metabolism. The tertiary KEGG pathway analysis showed that the main pathways of differentially metabolite action were amino acid metabolism (tyrosine metabolism, cysteine ​​and methionine metabolism, D-amino acid metabolism, arginine and proline metabolism), nucleotide metabolism (purine and pyrimidine metabolism), and lipid and inflammation-related metabolism (arachidonic acid metabolism).

[0050] In summary, aging-related type 2 diabetes can induce a gut differential metabolic network characterized by amino acid metabolism reprogramming, abnormal nucleotide turnover, lipid / inflammation-related metabolic imbalance, and gut microbiota and host co-metabolic disorders. Metformin and NP intervention, especially NPH, can reshape the above key nodes and modules, causing metabolic homeostasis to change from the aging-related type 2 diabetes pattern to the intervention pattern.

[0051] Example 8: KEGG enrichment analysis of differential metabolites among multiple groups The specific pathways of differentially metabolites among multiple groups have been identified. Further KEGG enrichment analysis of these pathways revealed significantly enriched pathways. Results showed that pathways at the center of the pathway-metabolite network map were mainly clustered in amino acid metabolism (tyrosine metabolism, cysteine ​​and methionine metabolism, β-alanine metabolism, arginine and proline metabolism, alanine, aspartic acid, and glutamate metabolism), nucleotide metabolism (pyrimidine metabolism), lipid-inflammatory metabolism (arachidonic acid metabolism), vitamin / cofactor metabolism (retinol metabolism, vitamin digestion and absorption, cofactor biosynthesis, niacin and nicotinamide metabolism, and vitamin B6 metabolism), and basal energy metabolism (oxidative phosphorylation, the tricarboxylic acid cycle, and central carbon metabolism in cancer). This indicates significant pathway crossover relationships among differentially metabolites. Furthermore, aging-related type 2 diabetes and intervention effects contribute to nitrogenous substrate utilization and amino acid co-metabolism network reconstruction, and also participate in nucleotide turnover, cell renewal residues, and gut microbiota nucleic acid substrate processing. Significant inflammatory lipid mediator-related signals were found in the differentially metabolites among multiple groups, suggesting a possible link between intestinal metabolic imbalance and chronic inflammation. In addition, aging-related type 2 diabetes and different interventions also affect vitamin absorption, coenzyme production and redox assist systems, and influence energy metabolism and central carbon flux distribution in the gut environment.

[0052] KEGG enrichment analysis revealed that tyrosine metabolism, nucleotide metabolism, retinol metabolism, protein digestion and absorption, and arachidonic acid metabolism were the main pathways with significant enrichment of differentially expressed metabolites among multiple groups. KEGG topology analysis showed that retinol metabolism was the most significant, suggesting that retinol metabolism may not only be a concomitant change but also an important functional module connecting intestinal epithelial homeostasis, mucosal immunity, and abnormal vitamin absorption. Other pathways with high influence values ​​in the enrichment results included arginine and proline metabolism, β-alanine metabolism, and alanine, aspartic acid, and glutamate metabolism, indicating that these pathways may also occupy critical positions in the metabolic network. Aging-related type 2 diabetes may induce abnormalities in a multi-level intestinal metabolic network characterized by amino acid metabolic reprogramming, abnormal nucleotide turnover, altered lipid inflammatory mediators, vitamin / cofactor metabolic imbalance, and intestinal barrier immune-related dysfunction. Metformin and NP intervention may promote a shift in intestinal metabolic homeostasis from a disease-based to an intervention-based model by reshaping some key metabolic nodes in the aforementioned network.

[0053] Example 9: Correlation and weighted gene co-expression network analysis of total metabolites, biochemical indicators, and behavioral epigenetics To establish the link between physiological indicators, oxidative stress-related indicators, inflammatory markers, ALT, AST, blood glucose-related indicators, behavioral epigenetics, and metabolomics, this study analyzes the "bridge" between changes in metabolites and host biology to reveal the relationship between the gut microenvironment and the overall body epigenetics. The results are as follows: Figure 6As shown in the figure, correlation analysis between total metabolites and various phenotypic indicators revealed a clear clustering phenomenon among different phenotypes. Insulin resistance, blood glucose, ALT / AST, inflammatory factors, and oxidative stress-related indicators were mostly distributed in similar branches, while behavioral indicators such as OFT, EPM, MWM, and NOR clustered relatively in another branch, and these two branches often showed opposite correlations. This indicates that metabolic disorders, inflammation, oxidative stress, and behavioral impairment do not occur independently but are interconnected and mutually influential. The distribution of total metabolites also showed clear clustering characteristics, and the relationships between different metabolite clusters and phenotypic indicators were inconsistent. Some metabolite clusters were positively correlated with insulin resistance, hyperglycemia, inflammation, and oxidative stress indicators, while negatively correlated with behavioral improvement indicators; others were the opposite, tending to be associated with better behavioral performance, lower inflammation levels, and stronger antioxidant status, and negatively correlated with hyperglycemia or organ damage indicators. This indicates that these metabolite clusters do not have the same significance in the disease state; some are more likely to be associated with disease exacerbation, while others may be related to the recovery process. These results suggest that what is coupled to the disease phenotype is not a single metabolite, but rather a set of interconnected metabolites aligned in the same direction. This set of metabolites includes multiple levels of metabolism, including membrane lipid metabolism, bile acid metabolism, aromatic amino acid metabolism, inflammatory lipid mediator metabolism, and microbiota-host co-metabolism.

[0054] Weighted Gene Co-expression Network (WGCNA) analysis clusters metabolites with similar expression levels into modules. These modules are then linked to phenotypic correlations to construct co-expression network relationships, thereby identifying hub metabolites within each module and enabling disease-metabolite association analysis. This is illustrated in the WGCNA module-trait correlation heatmap. Figure 7 and Figure 8The total metabolites were divided into nine modules: MEgreen (233 metabolites), MEblack (84 metabolites), MEblue (399 metabolites), MEturquoise (1202 metabolites), MEpink (73 metabolites), MEred (138 metabolites), MEbrown (380 metabolites), MEyellow (354 metabolites), and MEgrey (74 metabolites). The gray modules represent metabolites that were not assigned to a specific module. In addition, different modules showed differentiated correlation patterns with different phenotypic dimensions. Moreover, most correlations were not driven by a single indicator, but rather appeared in clusters along multiple levels of "glucose metabolism - inflammation / oxidative stress - behavior". This suggests that the total metabolites in mouse feces and phenotype do not change independently, but rather are co-expressed or co-variant. The metabolic abnormalities in aging type 2 diabetic mice can be divided into many functional modules. Different modules correspond to different dimensions of the aging type 2 diabetic phenotype, rather than all metabolites reflecting the entire phenotype in the same direction.

[0055] The MEgreen and MEblue modules showed a high correlation with the phenotype and a significant p-value, indicating that MEgreen and MEblue are characteristic modules of the phenotype, containing 233 and 399 hub metabolites, respectively. Further, a metabolite set was created from the hub metabolites in the characteristic modules to obtain the phenotype characteristic metabolites (632 kinds).

[0056] Example 10: Relative expression levels of phenotypic metabolites in each group and analysis of the KEGG functional pathway. Further functional analyses of VIP and KEGG were performed on the phenotypic metabolites, and the results are as follows: Figures 9-10 As shown. VIP analysis shows ( Figure 9 (A) Among the phenotypic metabolites, 24,25-diacetyl-urgaloside and gentamicin sulfate A showed high distinguishing contributions, suggesting they may be important candidate molecules among phenotypic metabolites. The heatmap further showed that these high-VIP metabolites exhibited different expression patterns in the Control, Model, Met, NPL, and NPH groups, suggesting that phenotypic metabolites simultaneously contain disease-related and intervention-response-related metabolic features. Figure 9 (B) The KEGG secondary classification results show that the phenotypic metabolites mainly belong to the metabolic category, among which amino acid metabolism, lipid metabolism, cofactor and vitamin metabolism, carbohydrate metabolism and nucleotide metabolism account for a relatively high proportion. Figure 10KEGG three-level pathway analysis revealed that phenotypic metabolites were mainly enriched in multiple pathways, including arginine and proline metabolism, D-amino acid metabolism, histidine metabolism, lysine degradation, tyrosine metabolism, and β-alanine metabolism. This suggests that phenotypic metabolites can highly reflect the utilization of nitrogenous substrates in the gut and the co-metabolism of amino acids by the host and gut microbiota. Furthermore, the enrichment in ABC transport, protein digestion and absorption, and bile secretion indicates that phenotypic metabolites are involved not only in the metabolic end product profile but also in substrate supply, bile-mediated intestinal environment, and transmembrane transport processes. The enrichment in glycerophospholipid metabolism and arachidonic acid metabolism suggests that phenotypic metabolites are related to membrane lipid homeostasis and inflammatory lipid mediator signaling. The enrichment in cofactor biosynthesis, nucleotide metabolism, and purine metabolism suggests that phenotypic metabolites are involved in cofactor generation, nucleotide turnover, and basal anabolic metabolism. In summary, phenotypic metabolites constitute a complex intestinal metabolic network, primarily centered on amino acid metabolism, while also being related to lipid metabolism, nucleotide metabolism, substance transport, and digestion and absorption. Especially in the age-related type 2 diabetes state, metformin and NP intervention may affect metabolic damage, inflammation, oxidative stress and related behavioral phenotypes by reconstructing nitrogenous metabolism, membrane metabolism and metabolites and metabolic pathways involved in the transport environment.

[0057] Example 11: KEGG pathway enrichment analysis of phenotypic metabolites Further KEGG enrichment analysis was performed on the functional pathways of phenotypic metabolites, and the results are as follows: Figures 11-12As shown, phenotypic metabolites are mainly concentrated in pathways such as protein digestion and absorption, amino acid metabolism, ABC transport, nucleotide metabolism, bile secretion, and sphingolipid signaling, suggesting that age-related intestinal metabolic imbalances associated with type 2 diabetes may be due to fermentation products, epithelial barrier status, and the co-metabolic relationship between gut microbiota and the host. The co-enrichment of protein digestion and absorption, ABC transport, and multiple amino acid metabolic pathways suggests alterations in the supply, transport, and utilization of protein or amino acid substrates in the gut, leading to changes in the nitrogenous metabolic network represented by arginine and proline metabolism, histidine metabolism, β-alanine metabolism, and D-amino acid metabolism. The enrichment of nucleotide metabolism suggests that disease and intervention not only affect nutrient substrate metabolism but also involve changes in intestinal epithelial renewal, microbial material turnover, and metabolic stress. Simultaneously, the enrichment of bile secretion, linoleic acid metabolism, and sphingolipid signaling pathways suggests that the bile acid environment, membrane lipid homeostasis, and inflammatory lipid signaling also participate in the formation of phenotypic metabolites. Notably, neuroactive metabolites such as histamine, indole, choline, octopamine, and palmitoylethanolamide simultaneously entered the enrichment network and connected to pathways such as neuroactive ligand-receptor interactions and glutamatergic synapses, suggesting that abnormal gut metabolism may also participate in the formation of behavioral phenotypes through gut-brain axis-related molecules. In summary, the phenotypic metabolites reveal not just changes in a single metabolic pathway, but a complex metabolic state alteration caused by changes in gut nutrient substrate utilization, inflammation repair, bile and lipid signaling environments, and shared metabolic changes in the gut microbiota and host. This alteration may be an important material basis for linking age-related type 2 diabetes-related biochemical abnormalities and behavioral impairments.

[0058] Example 12 Association between total metabolites and gut microbiota and WGCNA analysis Alterations in gut microbiota inevitably lead to changes in related metabolites. These changes are not merely alterations of single metabolites, but rather changes in corresponding metabolic modules or clusters. Therefore, this study investigated the modularity, directionality, and functional specialization of gut microbiota and total metabolites. The results are as follows: Figures 13-15 As shown in the heatmap, the correlation between metabolites and gut microbiota is illustrated. Figure 13 The association between the two is not discrete, but rather exhibits a clear clustering characteristic. That is, a group of bacterial genera is often associated with one cluster of metabolites in the same direction, while changing in the opposite direction with another cluster of metabolites. This suggests that the gut microbiota does not affect a single isolated metabolite, but is more likely to be several metabolic clusters with a common substrate source and biological function.

[0059] Further WGCNA analysis revealed that total metabolites could be organized into multiple covariant modules, which showed differential correlations with different bacterial genera, indicating that the influence of gut microbiota on metabolism is manifested in the form of functional modules rather than single metabolites. Figure 14 and Figure 15In the aforementioned analysis, a significant correlation has been confirmed between the total metabolite module and the host's biochemical and behavioral phenotypes. Pathway analysis revealed that the metabolites characteristic of this phenotype are mainly involved in protein digestion and absorption, ABC transport, amino acid metabolism, nucleotide metabolism, bile secretion, and lipid signaling pathways. This indicates that substrate supply, bile acid environment, epithelial barrier status, and inflammatory oxidative stress levels in the gut are altered in the aging type 2 diabetes state. Furthermore, based on the previous chapters' research on gut microbiota dysbiosis in the context of aging-related type 2 diabetes, it is inferred that the niche and competitive landscape of the gut microbiota have changed. These changes in microbiota structure further affect protein / amino acid substrate utilization, lipid and bile acid conversion, and the generation and consumption of aromatic and nitrogenous metabolites, ultimately leading to a modular reconstructing of total metabolites. On the other hand, since these metabolic modules are simultaneously related to host biochemical indicators and behavioral phenotypes, it is speculated that the gut microbiota does not directly determine a single host phenotype. Instead, it participates in changes in amino acid metabolism, nucleotide metabolism, bile acid metabolism, lipid metabolism, and substance transport processes by reconstructing metabolic modules that change synchronously with the phenotype of aging-related diabetes, thereby further contributing to age-related type 2 diabetes-related metabolic damage, inflammation, oxidative stress, and behavioral abnormalities.

[0060] Metabolic sets were created from hub metabolites in metabolic modules that showed significant correlation with gut microbiota in the WGCNA analysis, resulting in 483 characteristic metabolites of gut microbiota. The main characteristic modules of gut microbiota in the WGCNA analysis were MEbrown and MEpink, which contained 391 and 92 hub metabolites, respectively.

[0061] Example 13 Analysis of VIP and KEGG pathways in gut microbiota characteristic metabolites Based on WGCNA analysis, this embodiment further analyzed the expression levels and pathways of gut microbiota-specific metabolites. Unlike metabolites that are simply differentially expressed between groups, gut microbiota-specific metabolites are more likely to reflect co-metabolic processes involved in or regulated by the gut microbiota. The results are as follows: Figure 16 As shown, various gut microbiota-specific metabolites exhibited clear differences in expression patterns among the five groups, and some metabolites had high VIP values ​​(Variants Value Value). Figure 16(A) suggests that it makes a strong contribution to the separation between groups and may be a representative candidate molecule among the characteristic metabolites of gut microbiota. More importantly, the heatmap does not reflect the individual increase or decrease of a single metabolite in a certain group, but rather shows several metabolic clusters with common changes: some metabolites are generally increased in the Model group and some intervention groups, but lower in the Control group; others show the opposite trend, suggesting that the characteristic metabolites of gut microbiota do not originate from independent metabolic reactions, but are more likely to represent a group of metabolic clusters driven by common upstream factors. That is to say, under the dual effects of aging and diabetes, the structure of the mouse gut microbiota is restructured, which has a collective impact on certain substrate utilization and metabolic flux, thereby causing the characteristic metabolites of gut microbiota to form metabolic clusters and consistent directions.

[0062] The results of the secondary classification of the KEGG pathway indicate that ( Figure 16 (B) The characteristic metabolites of the gut microbiota mainly belong to the metabolism-related category, among which lipid metabolism, amino acid metabolism, cofactor and vitamin metabolism, and exogenous biodegradation and metabolism account for a relatively high proportion. At the same time, they involve functional backgrounds of the digestive system, nervous system, immune system and endocrine system, suggesting that the characteristic metabolites of the gut microbiota may be a complex functional module composed of nitrogenous substrate utilization, membrane lipid / lipid environment changes and cofactor metabolism.

[0063] Further tertiary analysis of the KEGG pathway showed that ( Figure 17 The characteristic metabolites of the gut microbiota are mainly enriched in pathways such as cofactor biosynthesis, arachidonic acid metabolism, linoleic acid metabolism, cysteine ​​and methionine metabolism, tryptophan metabolism, protein digestion and absorption, bile secretion, ABC transporters, aminoacyl-tRNA biosynthesis, neuroactive ligand-receptor interactions, and ferroptosis. In particular, protein digestion and absorption and amino acid metabolism pathways are observed simultaneously. These results suggest that the characteristic metabolites of the gut microbiota likely reflect changes in the supply and utilization of protein / amino acid substrates in the gut under the background of aging-related type 2 diabetes, as well as a complex metabolic remodeling characterized by lipid inflammatory signaling, bile acid / substance transport environment, cofactor metabolism, some neuroactive metabolism, and ferroptosis redox metabolism. Combined with previous results on changes in gut microbiota structure and the correlation between total metabolic modules and host phenotypes, it can be inferred that gut microbiota imbalance may synergistically change with the above metabolic processes and jointly participate in the formation and maintenance of age-related type 2 diabetes-related metabolic damage, inflammatory oxidative stress, and behavioral abnormalities.

[0064] Example 14: KEGG pathway enrichment analysis of characteristic metabolites of gut microbiota Further enrichment analysis of the above KEGG pathways yielded the following results: Figure 18 As shown in the KEGG enrichment analysis network diagram ( Figure 18(A)), gut microbiota-specific metabolites form connections with multiple KEGG pathways, mainly involving linoleic acid metabolism, arachidonic acid metabolism, glutathione metabolism, ferroptosis, cysteine ​​and methionine metabolism, tryptophan metabolism, protein digestion and absorption, neuroactive ligand-receptor interactions, serotonergic synapses, cytochrome P450-mediated exogenous substance metabolism, cofactor biosynthesis, endocannabinoid reverse signaling, platelet activation, and thyroid hormone synthesis. KEGG enrichment bubble plots show ( Figure 18 (B) This metabolic set is not disordered and dispersed, but rather mainly concentrated in several functional processes with clear biological connections, including linoleic acid metabolism, ferroptosis, arachidonic acid metabolism, and tryptophan metabolism pathways. This suggests that the characteristic metabolites of the gut microbiota are mainly involved in unsaturated fatty acid metabolism, lipid inflammatory mediator generation, membrane lipid oxidative damage, and redox homeostasis imbalance. Simultaneously, glutathione metabolism, cysteine ​​and methionine metabolism, and the ferroptosis pathway are enriched, indicating that these metabolites are closely related to sulfur-containing metabolism, glutathione-dependent antioxidant defense, and lipid peroxidation damage. Therefore, abnormal gut microbiota is likely to synergistically interact with host oxidative stress and membrane lipid oxidative damage. Furthermore, Figure 19 The enrichment of tryptophan metabolism suggests that aromatic amino acid co-metabolism is also an important component of gut microbiota-specific metabolites. Tryptophan and its indole derivatives are generally considered representative metabolic axes with deep gut microbiota involvement. This result suggests that changes in gut microbiota are likely accompanied by a readjustment of aromatic amino acid metabolism, further affecting immune homeostasis and the neuroactive molecular environment. In other words, microbiota remodeling may simultaneously alter the utilization of nitrogenous substrates and the indole metabolic background. Meanwhile, pathways such as protein digestion and absorption, cofactor biosynthesis, and cytochrome P450-mediated exogenous substance metabolism were also enriched, indicating that gut microbiota-specific metabolites may also be closely related to the supply and subsequent utilization of protein / amino acid substrates in the intestinal lumen, basic cofactor metabolic support, and the conversion of exogenous small molecules. Notably, neuroactive ligand-receptor interactions, serotonergic synapses, endocannabinoid reverse signaling, and oxytocin signaling pathways were also annotated in this metabolite set, suggesting the inclusion of some small molecules with a neuroactive background. This provides a potential metabolic basis for the link between gut microbiota changes and behavioral phenotypes.

[0065] In summary, the characteristic metabolites of the gut microbiota do not reflect abnormalities in a single metabolic pathway. Rather, they may represent a complex metabolic network remodeling in the context of aging-related type 2 diabetes, which is accompanied by changes in aromatic and nitrogenous metabolism, enhanced lipid inflammatory signaling, oxidative stress imbalance, and gut-brain axis-related metabolic changes.

[0066] In the foregoing analysis, this invention has discovered a significant coupling relationship between gut microbiota composition, total metabolite modules, and host biochemical and behavioral phenotypes in aging-related type 2 diabetes. Further pathway analysis indicates that these phenotypic metabolites mainly involve pathways related to protein digestion and absorption, amino acid metabolism, nucleotide metabolism, bile secretion, lipid signaling transduction, and neuroactive molecules. This suggests that aging-related type 2 diabetes-related metabolic abnormalities are not merely general nutritional metabolic imbalances, but are also closely related to inflammatory oxidative stress, changes in the lipid microenvironment, and small molecule background remodeling associated with behavioral phenotypes. In other words, phenotypic metabolites reflect the most sensitive changes in host phenotype to metabolic abnormalities. Based on this, this invention further screened and constructed gut microbiota-specific metabolites and conducted KEGG pathway enrichment analysis on them. Compared to phenotypic metabolites, gut microbiota-specific metabolites are more focused on metabolic processes within a specific gut microbiota context, including linoleic acid metabolism, arachidonic acid metabolism, glutathione metabolism, ferroptosis, cysteine ​​and methionine metabolism, tryptophan metabolism, protein digestion and absorption, neuroactive ligand-receptor interactions, and serotonergic synapses. This indicates that microbiota-related metabolic abnormalities are not simply a repetition of overall host metabolic abnormalities, but rather reflect the specific metabolic impacts of gut microbiota remodeling.

[0067] Phenotypic metabolites and gut microbiota-specific metabolites show significant functional overlap, primarily in three areas. First, there is a remodeling of nitrogenous and aromatic substrate metabolism, including protein digestion and absorption, tryptophan metabolism, and cysteine ​​and methionine metabolism. This suggests that in aging-related type 2 diabetes, the structure of protein / amino acid substrates available to the gut microbiota may have changed, and microbiota remodeling further affects the balance of substrate production, transformation, and consumption, thus forming a relatively clear shared metabolic fingerprint. Second, there is an enhanced background of lipid inflammation and membrane lipid oxidation, mainly manifested in the simultaneous occurrence of pathways such as linoleic acid metabolism, arachidonic acid metabolism, glutathione metabolism, and ferroptosis. This indicates that metabolic abnormalities related to changes in the gut microbiota do not stop at the level of general fermentation products but extend further to the generation of lipid inflammatory mediators, altered redox homeostasis, and the degree of membrane lipid damage. Third, there is a remodeling of the neuroactive small molecule environment, manifested in the annotation of pathways such as neuroactive ligand-receptor interactions, serotonergic synapses, and endocannabinoid reverse signaling. Based on the correlation results between the total metabolic module and behavioral indicators mentioned above, it can be inferred that intestinal metabolic abnormalities may act as an important intermediary link between gut microbiota imbalance and behavioral phenotypes by altering small molecules in the neuroactive background. From a holistic mechanism perspective, aging-related type 2 diabetes first leads to a rearrangement of the gut microbiota structure, subsequently altering the utilization of intestinal substrates, lipid and redox environments, as well as aromatic and sulfur-containing metabolic processes, ultimately forming a set of gut microbiota-specific metabolites significantly correlated with the gut microbiota. Among these, some gut microbiota-specific metabolites are closely related to host blood glucose, inflammatory oxidative stress, and behavioral phenotypes. Therefore, gut microbiota-specific metabolites are closer to the upstream driving layer, while phenotypic metabolites are closer to the downstream phenotypic mapping layer; together, they constitute the "microbiota-metabolic network-host phenotype" framework.

[0068] Overall, phenotypic metabolites and gut microbiota-specific metabolites have significant functional overlap. Age-related type 2 diabetes-related gut abnormalities are the result of a coordinated imbalance between gut microbiota, metabolic network, and host phenotype. Metformin and NP interventions may not work by correcting a single bacterial genus or metabolite, but rather by reshaping key nodes and functional modules in the gut microbiota-related metabolic network, causing simultaneous adjustments in upstream flora, phenotypic metabolites, and host phenotype, ultimately driving a shift in gut metabolic homeostasis from a disease-based to an intervention-based approach.

[0069] Example 15: KEGG regulatory network analysis of differentially expressed metabolites, phenotypic metabolites, and gut microbiota-specific metabolites among multiple groups. To further clarify the position of differentially metabolites among multiple groups in the disease-related metabolic network, and their functional connections and synergistic mechanisms with phenotypic metabolites and gut microbiota characteristic metabolites, this embodiment further constructed KEGG regulatory networks for differentially metabolites among multiple groups, phenotypic metabolites, and gut microbiota characteristic metabolites, respectively. Through comprehensive observation of the node types, connection strengths, and functional attributions of the two KEGG regulatory networks, the "bridging positions" of differentially metabolites among multiple groups in the overall pathological network can be more clearly identified, as shown in the following results. Figure 20-21 As shown. The KEGG regulatory network of differentially metabolites and phenotypic characteristic metabolites among multiple groups reveals (…). Figure 20 The two are embedded in a complex regulatory network composed of multiple KEGG pathway nodes, enzyme nodes, reaction nodes, and compound nodes, mainly including map00480, map04115, map00561, map04961, map04142, map00511, map05219, map01232, map00780, map05231, map04211, and map04612. This network is interconnected through multiple shared reactions and intermediate substrates, suggesting a significant functional synergistic relationship between differential metabolites and phenotypic characteristic metabolites among multiple groups.

[0070] From the perspective of network structure, this network does not present changes in a single local metabolic pathway, but rather resembles a comprehensive metabolic framework formed by the imbalance of multiple related metabolic processes. In particular, nodes such as map00480, map04115, map00561, map04961, and map04142 are located at the network center and intersect with other pathway nodes through common intermediate substrates and enzyme reactions. This indicates that the differentially metabolites screened in the multi-group comparative analysis do not directly and linearly map the host phenotype, but rather form structural connections with phenotypic metabolites by jointly embedding several key metabolic processes. Nodes map05231, map04211, and map04612 also participate in the construction of the network skeleton, suggesting that the connection between differentially metabolites and phenotypic metabolites among multiple groups is not limited to general metabolic substrate transport, but may also involve higher-level signal regulation and disease-related functional networks. Therefore, the nodes of phenotypic metabolites and differentially metabolites in the network represent a set of key functional nodes in the process of mapping differential metabolic abnormalities to the host phenotype. In summary, the KEGG regulatory network of differentially expressed metabolites and phenotypic metabolites across multiple groups suggests that age-related metabolic abnormalities in type 2 diabetes are not driven by changes in a single metabolite, but rather by cross-connections of multiple functional pathways, forming a complex regulatory network composed of differentially expressed metabolites, key enzymatic reactions, and phenotypic metabolites. This result further supports the notion that phenotypic abnormalities such as host blood glucose, inflammation, oxidative stress, and behavioral impairment correspond to isolated metabolic molecules, but rather to metabolic outcomes resulting from synergistic imbalances across multiple pathways.

[0071] Compared with the networks mentioned above, the KEGG regulatory network constructed from differentially metabolites among multiple groups and gut microbiota-specific metabolites exhibits more pronounced clustering and functional unitization characteristics. Figure 21 The network shows that multiple highly connected pathway nodes are concentrated in several functional subregions, mainly involving glycolipid complex metabolism, sphingolipid and membrane lipid pathways, fat digestion and absorption, protein digestion and absorption, ferroptosis, sulfur-containing metabolism, and some cofactor metabolism-related backgrounds. A large number of enzyme nodes, reaction nodes, and compound nodes are clustered around some pathway nodes, and they are connected to other functional clusters through a few key substrate nodes, suggesting a more pronounced modular characteristic of the network.

[0072] From an overall structural perspective, this network exhibits less widespread and dispersed pathway crossover than networks of differential metabolites and phenotypic metabolites among multiple groups. Instead, it is characterized by several functional clusters revolving around a few core pathway nodes. This suggests that gut microbiota-specific metabolites are more likely to represent functional metabolic units driven by changes in the gut microbiota, with stronger upstream constraints and more concentrated functional attributions. Combined with the KEGG enrichment results of gut microbiota-specific metabolites, these network nodes are mainly organized around processes such as lipid inflammatory signaling, redox homeostasis, co-metabolism of sulfur-containing and aromatic amino acids, and changes in substrate utilization. Therefore, this network is more microbiota-driven. Based on this result, it is indicated that the connection between differential metabolites obtained from multiple group comparisons and gut microbiota-specific metabolites is not established through scattered single-molecule correlations, but rather through the common embedding of several metabolic functional modules with a microbiota background. In other words, gut microbiota remodeling does not affect all metabolic processes evenly, but is more likely to preferentially act on the metabolic network related to the membrane lipid environment, glycolipid substrates, protein or amino acid utilization, sulfur-containing metabolism, and oxidative damage. The current network shows an aggregation of nodes related to ferroptosis, fat and protein digestion and absorption, glycosphingolipids, or sphingolipids, suggesting that gut microbiota-related metabolic abnormalities have extended from the level of general fermentation products to lipid inflammatory mediators, membrane lipid homeostasis, and oxidative stress.

[0073] In summary, changes in the gut microbiota lead to abnormalities in its microecological-related metabolic functional units. Differential metabolites, acting as crucial intermediaries, participate in multiple functional pathways within this regulatory network, connecting gut microbiota drivers and host phenotypic responses. In other words, differential metabolites obtained from multiple group comparisons participate in constructing a continuous abnormal network of gut microbiota-metabolic network-host phenotype associated with aging and type 2 diabetes by collectively embedding themselves in multiple functional pathways. The effects of drugs and NP interventions are more likely not achieved by correcting a single bacterial genus or metabolite, but rather by reshaping key nodes and functional modules within this continuous network, shifting the overall "microbiota-metabolism-phenotype" coupling relationship towards a healthier state.

[0074] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. The application of NP in the preparation of intestinal metabolic regulators for type II diabetes mellitus, characterized in that, The NP is a nanoscale complex formed by hydrogen bonding of turtle egg peptide with a molecular weight of less than 3 kDa and naringenin at a mass ratio of 100:

1. The formulation reshapes the intestinal metabolic regulation network with differential metabolites as the bridge by regulating phenotype-related metabolic modules and intestinal microecology-related metabolic modules, thereby systematically improving age-related type II diabetes-related metabolic disorders.

2. The application as described in claim 1, characterized in that, The phenotype-related metabolic module includes the MEgreen module and the MEblue module, which contain a total of 632 hub metabolites. The metabolic pathways involved in the phenotype-related metabolic module mainly include protein digestion and absorption, amino acid metabolism, ABC transport, nucleotide metabolism, bile secretion, and sphingolipid signal transduction.

3. The application as described in claim 1, characterized in that, The gut microbiota-related metabolic module includes the MEbrown module and the MEpink module, which together contain 483 hub metabolites. The metabolic pathways involved in the gut microbiota-related metabolic module mainly include linoleic acid metabolism, arachidonic acid metabolism, glutathione metabolism, ferroptosis, cysteine ​​and methionine metabolism, tryptophan metabolism, protein digestion and absorption, neuroactive ligand-receptor interactions, and serotonergic synapses.

4. The application as described in claim 1, characterized in that, The intestinal metabolic regulatory network includes a first regulatory network between differential metabolites and phenotype-related metabolic modules, and a second regulatory network between differential metabolites and intestinal microecology-related metabolic modules.

5. The application as described in claim 4, characterized in that, The first regulatory network includes pathway nodes map00480, map04115, map00561, map04961, map04142, map00511, map05219, map01232, map00780, map05231, map04211, and map04612; the second regulatory network mainly involves glycolipid complex metabolism, sphingolipid and membrane lipid-related pathways, fat digestion and absorption, protein digestion and absorption, ferroptosis, and sulfur-containing metabolism.

6. The application as described in claim 1, characterized in that, The gut microbiota-related metabolic module is the upstream driving layer, the phenotype-related metabolic module is the downstream output layer, and the differential metabolites serve as a bridge connecting the upstream driving layer and the downstream output layer.

7. The application as described in claim 1, characterized in that, The formulation improves metabolic disorders associated with blood glucose, insulin resistance, inflammation, oxidative stress, liver function impairment, and behavioral phenotypes by reshaping the intestinal metabolic regulatory network.

8. The application as described in claim 1, characterized in that, The turtle egg-derived peptide is a peptide component with a molecular weight of less than 3 kDa obtained by centrifugation, filtration and ultrafiltration after papain enzymatic hydrolysis of freeze-dried turtle egg powder; the preparation method of the complex includes: mixing and dissolving the peptide component with naringenin at a mass ratio of 100:1, and forming the nanoscale complex by hydrogen bonding.

9. The application as described in claim 1, characterized in that, The type 2 diabetes mellitus of aging is type 2 diabetes mellitus induced by D-galactose combined with streptozotocin, the preparation is an oral preparation, and the effective dose of the complex is 0.5-1 g / kg·bw.

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