Citrus aurantium and sand-blanched small yellow ginger composition, preparation method thereof and application of composition in preparation of medicine for preventing and treating obesity

Abstract

The invention discloses a citrus aurantium flower and sand blanched small yellow ginger composition as well as a preparation method and application thereof in preparation of medicines for preventing and treating obesity. According to the invention, the sand blanching process of the small yellow ginger is optimized, the extraction rate of medicinal components is improved, the sand blanched small yellow ginger and the bitter citrus immature flower extract are combined according to different proportions for use, a high-fat diet induced obese animal model is established, the track of mouse weight is analyzed through animal experiment data, and the effects of different compatibility groups on preventing and treating obesity are researched; and determining the optimal compatibility ratio, and discussing and analyzing the fat-reducing action mechanism of the compatibility of the sand-blanched small yellow ginger and the citrus aurantium through lipid metabonomics and intestinal microbiomics.

Description

Technical Field

[0001] The present invention relates to the field of plant compositions and their activity evaluation, and particularly relates to a composition of bitter orange flower and stir-fried with sand small yellow ginger, a preparation method thereof, and uses in the preparation of drugs for preventing and treating obesity. Background Art

[0002] Obesity is one of the most common metabolic disorders globally and is associated with various diseases, including diabetes, coronary heart disease, hypertension, and breast cancer. Obesity is also associated with chronic inflammation, increasing the risk of cancer.

[0003] In the treatment of obesity, although significant progress has been made in medical interventions, there is currently no single solution to address the obesity problem. In recent years, some new weight loss drugs have been approved, such as GLP-1 receptor agonists (e.g., semaglutide) and combination therapies (e.g., phentermine / topiramate). Despite their remarkable efficacy, these drugs still have certain limitations and side effects. For example, GLP-1 receptor agonists may cause problems such as nausea, vomiting, and gastrointestinal discomfort, while the phentermine / topiramate combination therapy may pose cardiovascular risks, cognitive impairments, or other adverse reactions. In addition, the high cost of these drugs may also limit their widespread use.

[0004] Developing effective and safe anti-obesity compositions from medicinal and edible homologous plants is a promising approach. These plants usually contain bioactive compounds that can help control body weight and have fewer side effects compared to synthetic drugs. Bitter orange flower (scientific name: Citrus aurantium L. var. amara Engl) is a medicinal and edible homologous plant used to relieve indigestion, while stir-fried with sand small yellow ginger is a processed product of small yellow ginger (scientific name: Globba racemosa Sm.), and the efficacy of small yellow ginger obtained under different sand stir-frying conditions varies greatly. Stir-fried with sand small yellow ginger has the effects of relieving exterior syndrome, warming the middle-jiao to increase energy (spleen and stomach), and stopping vomiting. Most digestive drugs have the ability to strengthen the spleen and reduce fat. Increasing evidence shows that bitter orange flower and stir-fried with sand small yellow ginger have the potential for fat reduction.

[0005] The effectiveness of medicinal and edible homologous plants in intervening in obesity can be evaluated by monitoring changes in the lipid metabolite profile. Alterations in lipid metabolism are common in obese individuals. By understanding these pathways, the mechanisms of how obesity develops and progresses can be obtained, and potential weight loss targets of medicinal and edible homologous plants can be determined. In addition, the gut microbiota has been identified as a key factor affecting the occurrence and progression of obesity and obesity-related diseases, especially in terms of changes in its composition and metabolites during the progression of obesity. Understanding the metabolic interactions between the host and the gut microbiota can provide in-depth insights into how microbial metabolism affects obesity.

[0006] The present invention optimizes the sand-parching process of small yellow ginger, obtaining sand-parched small yellow ginger with the highest contents of active ingredients 6-gingerol, 8-gingerol, 10-gingerol, and gingerone. On this basis, different proportion combinations of sand-parched small yellow ginger and bitter orange flowers are made, and pharmacodynamic evaluation, lipid metabolomics, and fecal microbiome analysis are carried out on a mouse model induced by high fat, obtaining the optimal weight-loss combination ratio of sand-parched small yellow ginger and bitter orange flowers. The anti-obesity effect is related to lipid metabolism, primary bile acid biosynthesis, and the increase in the abundance of beneficial bacteria. Summary of the Invention

[0007] The object of the present invention is to provide a composition of bitter orange flowers and sand-parched small yellow ginger, its preparation method, and its use in the preparation of drugs for preventing and treating obesity. Both bitter orange flowers and small yellow ginger are plants with both medicinal and edible uses, and are used to aid gastric digestion. However, the combination of these two plants with both medicinal and edible uses has not been reported in terms of obesity prevention.

[0008] Sand-parching can affect the dissolution of the components of small yellow ginger, thus affecting its efficacy. The present invention first uses the Box-Behnken experimental design to optimize the sand-parching process of small yellow ginger, using 6-gingerol, 8-gingerol, 10-gingerol, and gingerone as optimization indicators to obtain the optimal sand-parching process of small yellow ginger.

[0009] At the same time, the present invention first uses an obese mouse model induced by a high-fat diet to evaluate the weight-loss efficacy of sand-parched small yellow ginger and bitter orange flowers in different proportions. The fat-reducing effect of the compatibility of sand-parched small yellow ginger and bitter orange flowers is studied through animal experiments to obtain the optimal compatibility ratio, and the fat-reducing mechanism of the optimal proportion composition of sand-parched small yellow ginger and bitter orange flowers is revealed by means of lipid metabolomics and 16sRNA intestinal flora detection.

[0010] The technical solution of the present invention is as follows:

[0011] A composition of bitter orange flowers and sand-parched small yellow ginger is a medicinal liquid obtained by water extraction using bitter orange flowers and sand-parched small yellow ginger as raw materials;

[0012] In the medicinal liquid, based on the raw materials, the mass ratio of sand-parched small yellow ginger to bitter orange flowers is 30:70 to 87:13, and particularly preferably 63:37;

[0013] In the medicinal liquid, based on the raw materials, the concentration of sand-parched small yellow ginger is 0.03 - 0.12 g / mL, and the concentration of bitter orange flowers is 0.0175 - 0.07 g / mL.

[0014] The preparation method of the composition of bitter orange flowers and sand-parched small yellow ginger of the present invention is as follows:

[0015] Mix the bitter orange flowers with pure water, reflux and extract at 100 °C for 60 min, and concentrate to obtain the bitter orange flower extract; mix the stir-fried ginger with pure water, reflux and extract at 100 °C for 60 min, and concentrate to obtain the stir-fried ginger extract; mix the bitter orange flower extract and the stir-fried ginger extract according to the ratio, and the obtained medicinal liquid is the composition of bitter orange flowers and stir-fried ginger.

[0016] In the present invention, the processing technology of the stir-fried ginger is as follows:

[0017] Weigh the ginger and put it into river sand preheated to 180 - 220 °C for stir-frying for 4 - 8 min, then take it out and let it cool, and the stir-fried ginger is obtained;

[0018] Among them, the ratio of sand to medicine is 2 - 10.

[0019] Furthermore, in the present invention, taking 6-gingerol, 8-gingerol, 10-gingerol and gingerone as indicators, the Box-Behnken experimental design is used to optimize the stir-frying processing technology of ginger, and the specific steps are as follows:

[0020] S1. Take clean ginger slices, 50 g for each portion. According to the effects of different stir-frying conditions on gingerone, 6-gingerol, 8-gingerol and 10-gingerol, based on the Box-Behnken experimental design principle, select three factors: time, temperature, and the ratio of sand to medicine, and each factor takes three levels marked as -1, 0, 1. Use the response surface software Design Expert 10.0.3 for factor level design, and the experimental scheme is as follows:

[0021]

[0022] S2. Weigh the stir-fried ginger obtained under different stir-frying conditions in S1 respectively, powder it, pass the powder through a No. 3 sieve, accurately weigh it, put it into a conical flask respectively, add 30 mL of methanol, extract ultrasonically for 40 min, make up the loss with methanol, and filter through a 0.45 μm microporous filter membrane to obtain the test solution;

[0023] S3. Accurately weigh 1.23 mg, 2.76 mg, 2.02 mg and 1.83 mg of 6-gingerol, 8-gingerol, 10-gingerol and gingerone reference substances respectively, add 2 mL of methanol to each, ultrasonically treat for 8 min, take 0.5 mL from each, and filter through a 0.45 μm microporous filter membrane to obtain the reference substance stock solution;

[0024] S4. Accurately absorb the 6-gingerol, 8-gingerol, 10-gingerol and gingerone reference substance stock solutions to prepare a series of concentration mixed reference substance solutions, and inject and determine according to the following chromatographic conditions:

[0025] Chromatographic conditions: Use Thermo ODS C 18A chromatographic column (4.6 mm × 250 mm, 5 μm); mobile phase A is acetonitrile, and mobile phase B is an aqueous acetic acid solution with a volume fraction of 0.1%. Gradient elution: 0 - 10 min, 10 - 20% (volume fraction) A; 10 - 60 min, 20 - 100% (volume fraction) A; flow rate: 0.6 ml·min -1 ; detection wavelength: 280 nm; column temperature: 30 °C; injection volume: 10 μL; running time: 60 min; the number of theoretical plates is not less than 5000;

[0026] Taking the concentration of each reference substance as the abscissa and the peak area as the ordinate for linear regression analysis to obtain the standard curves of 6-gingerol, 8-gingerol, 10-gingerol, and gingerone;

[0027] S5. Using the chromatographic conditions in S4 to determine the test solution obtained in S2, substituting the peak areas in the chromatogram into the standard curves obtained in S4 to obtain the contents of gingerone, 6-gingerol, 8-gingerol, and 10-gingerol in the stir-fried small yellow ginger processed under different stir-frying conditions, and through the response surface method, the optimal stir-frying time is obtained as 6.4 min, the stir-frying temperature is 201 °C, and the ratio of sand to medicine is 6.6.

[0028] In the present invention, taking 6-gingerol, 8-gingerol, 10-gingerol, and gingerone as indicators, comparing the changes in the contents of the index components of the stir-fried small yellow ginger obtained under different stir-frying times, stir-frying temperatures, and ratios of sand to medicine in the stir-frying processing technology, using the Box-Behnken experimental design to optimize the processing technology, and taking the maximum values of gingerone, 6-gingerol, 8-gingerol, and 10-gingerol as the optimization objectives according to the regression equation model. According to the actual experimental conditions and verification, the optimal stir-frying processing technology for small yellow ginger is: stir-frying time 6.4 min, stir-frying temperature 201 °C, and ratio of sand to medicine 6.6.

[0029] The composition of bitter orange flower and stir-fried small yellow ginger of the present invention can be used to prepare drugs for preventing and treating obesity.

[0030] The present invention also studied the fat-reducing effects of different compatibility ratios of two medicinal plants, bitter orange flower and stir-fried small yellow ginger, and explored the optimal fat-reducing ratio of bitter orange flower and stir-fried small yellow ginger processed by the optimal process; the specific experimental steps are as follows:

[0031] S1. Prepare several compositions of bitter orange flower and stir-fried small yellow ginger with different ratios respectively, and use liquid chromatography-mass spectrometry technology to characterize the components of bitter orange flower and stir-fried small yellow ginger.

[0032] S2. An obese C57BL / 6J mouse model induced by a high-fat diet was established, and then mice were administered with compositions of Citrus aurantium L. Flos and stir-fried Zingiber officinale Rosc. with different ratios for 6 weeks. The change trajectory of the mouse body weight was analyzed, and hematoxylin and eosin staining (HE) of the liver and oil red O staining of adipose tissue were performed; ELISA was used to measure the levels of serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c); moreover, serum fatty acid metabolome analysis was carried out by UPLC-Q-TOF / MS analysis, and the intestinal flora was analyzed by 16S rRNA gene sequencing. Pattern recognition and Pearson correlation analysis were used to determine key endogenous metabolites and microbiota.

[0033] Through the above experiments, the optimal fat-reducing ratio was obtained: the mass ratio of stir-fried Zingiber officinale Rosc. to Citrus aurantium L. Flos was 63:37. Administering the composition for 6 weeks significantly prevented the increase in mouse body weight. This composition could significantly reduce the levels of serum total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-c), and significantly increase the level of high-density lipoprotein cholesterol (HDL-c).

[0034] The beneficial effects of the present invention are as follows:

[0035] The application of the combination of Citrus aurantium L. Flos and stir-fried Zingiber officinale Rosc. in obesity prevention was proposed for the first time, and by optimizing the stir-frying process of Zingiber officinale Rosc., the extraction rate of pharmaceutically active ingredients was improved.

[0036] In the present invention, the compositions of stir-fried Zingiber officinale Rosc. and Citrus aurantium L. Flos were formulated in different ratios, an obese animal model induced by a high-fat diet was established, the trajectory of the mouse body weight was analyzed through animal experiment data, the effects of different formulated groups on preventing and treating obesity were studied, and the optimal formulated ratio was obtained. Moreover, lipid metabolomics and intestinal microbiomics were used to explore and analyze the fat-reducing mechanism of the compatibility of stir-fried Zingiber officinale Rosc. and Citrus aurantium L. Flos.

[0037] According to Example 3, when the ratio between Citrus aurantium L. Flos and stir-fried Zingiber officinale Rosc. was 37:63, the best anti-obesity effect was achieved. The levels of TC and LDL-c in Group C3 were significantly reduced, while the level of TG in Group A1 was significantly reduced. The ratios of Citrus aurantium L. Flos to stir-fried Zingiber officinale Rosc. in both Group C3 and Group A1 were 37:63, but the dose of Group C3 was only 1 / 4 of that of Group A1. Interestingly, the level of HDL-c in Group C3 was significantly increased. HDL-c helps to remove cholesterol in arteries and reduce the risks of atherosclerosis and cardiovascular diseases. Compared with the model group, 16 and 25 biomarkers were respectively identified in Group C3 and Group A1, and these biomarkers were mainly related to lipid metabolism and primary bile acid biosynthesis. Description of the Drawings

[0038] Figure 1: Response surface plots and contour plots of time, temperature, and sand-drug ratio for zingerone.

[0039] Figure 2 : Response surface plots and contour plots of time, temperature, and sand-drug ratio for 6-gingerol.

[0040] Figure 3 : Response surface plots and contour plots of time, temperature, and sand-drug ratio for 8-gingerol.

[0041] Figure 4 : Response surface plots and contour plots of time, temperature, and sand-drug ratio for 10-gingerol.

[0042] Figure 5 : Schematic diagram of the experimental procedure.

[0043] Figure 6 : Effects of Citrus aurantium L. flowers, sand-roasted Zingiber officinale Rosc. rhizomes, and their 1:1 combination on four lipid parameters.

[0044] Figure 7 : Effects of different combinations of Citrus aurantium L. flowers and sand-roasted Zingiber officinale Rosc. rhizomes on body weight and four lipid parameters.

[0045] Figure 8 : Weight loss effect of the A1 group of Citrus aurantium L. flowers - sand-roasted Zingiber officinale Rosc. rhizomes.

[0046] Figure 9 : Weight loss effect of the C3 group of Citrus aurantium L. flowers - sand-roasted Zingiber officinale Rosc. rhizomes.

[0047] Figure 10 : Lipid metabolism pathway diagram.

[0048] Figure 11 : PcoA analysis of gut microbiota.

[0049] Figure 12 : F / B ratio and relative abundance.

[0050] Figure 13 : Correlation analysis. Detailed implementation manners

[0051] The present invention will be further described below through specific examples, but the protection scope of the present invention is not limited thereto.

[0052] In the following examples, the experimental medicinal materials Citrus aurantium L. flowers and Zingiber officinale Rosc. rhizomes were provided by Quzhou Yiniantang Co., Ltd.

[0053] Example 1 Optimization of the sand-roasting process of Zingiber officinale Rosc. rhizomes

[0054] Take clean slices of Zingiber officinale Rosc. rhizomes, 50 g per portion. According to the experimental design, put river sand in a container, heat the river sand and measure the temperature with an infrared thermometer. After reaching the set temperature, put the slices of Zingiber officinale Rosc. rhizomes into the sand for roasting and start timing. After reaching the set time, take them out and let them cool.

[0055] Weigh the stir-fried small yellow ginger powder obtained under different treatment conditions separately, sieve it through a No. 3 sieve, accurately weigh it, put it into a conical flask respectively, add 30 ml of methanol, extract it by ultrasonic for 40 min, make up the lost amount with methanol, filter it through a 0.45 μm microporous membrane to obtain the test solution.

[0056] Accurately weigh 1.23 mg, 2.76 mg, 2.02 mg, and 1.83 mg of 6-gingerol, 8-gingerol, 10-gingerol, and gingerone reference substances respectively, add them to 2 ml of methanol, treat them by ultrasonic for 8 min, take 0.5 ml each, filter it through a 0.45 μm microporous membrane to obtain the reference substance stock solution.

[0057] Use a Thermo ODS C18 (4.6 mm × 250 mm, 5 μm) chromatographic column; mobile phase A is acetonitrile, B is 0.1% acetic acid aqueous solution, and gradient elution method is adopted (0 - 10 min, 10% - 20% A; 10 - 60 min, 20% - 100% A). Flow rate: 0.6 ml·min -1 , Detection wavelength: 280 nm. Column temperature: 30 °C, injection volume: 10 μL, running time: 60 min, the number of theoretical plates should not be less than 5000.

[0058] Accurately pipette the 6-gingerol, 8-gingerol, 10-gingerol, and gingerone reference substance stock solutions respectively to prepare mixed reference substance solutions with different concentrations, and inject and determine according to the above chromatographic conditions. Perform linear regression analysis with the reference substance concentration as the abscissa (X) and the peak area as the ordinate (Y).

[0059] According to the effects of different treatment conditions on gingerone, 6-gingerol, 8-gingerol, and 10-gingerol, based on the Box-Behnken experimental design principle, select 3 factors that have a greater impact on gingerone, 6-gingerol, 8-gingerol, and 10-gingerol: time, temperature, and sand-drug ratio. Each factor takes three levels marked as -1, 0, and 1, and use the response surface software Design Expert 10.0.3 for factor level design, and then obtain the optimal conditions for gingerone, 6-gingerol, 8-gingerol. The factor level table is shown in Table 1-1.

[0060] Table 1-1 Experimental design table for stir-frying process

[0061]

[0062] The linear results are shown in Table 1-2. Gingerone, 6-gingerol, 8-gingerol, and 10-gingerol have good linear relationships within the corresponding concentration ranges.

[0063] Table 1-2 Linear equations

[0064]

[0065]

[0066] Through the Design Expert 10.0.3 data processing software, the data of each factor was input into the Box-Behnken data analysis software to obtain the response surface experiment design table. The experimental data were obtained according to the factor levels in the design table, and the results are shown in Tables 1-3.

[0067] Table 1-3 Response Surface Optimization Experiment Design and Results

[0068]

[0069] The experimental design and results of the response surface method are shown in Table 1-3. The Design expert 10.0.3 data analysis software was used to perform multiple regression fitting on the experimental data. Let time, temperature, and sand-drug ratio be A, B, and C respectively. Multiple regression fitting was performed with zingerone, 6-gingerol, and 8-gingerol as the response values to obtain the quadratic polynomial regression model:

[0070] Y 姜酮 = 0.15 + 0.005125*A + 0.003625*B + 0.017*C + 0.00175*AB - 0.0075*AC + 0.005*

[0071] BC - 0.006675*A 2 - 0.009675*B 2 - 0.023*C 2

[0072] Y 6-姜酚 = 2.28 + 0.11*A 0.035*B + 0.097*C - 0.06*AB - 0.088*AC + 0.026*BC - 0.11*A 2 - 0.17*

[0073] B 2 - 0.16*C 2

[0074] Y 8-姜酚 = 1.14 + 0.045*A + 0.02*B + 0.03*C - 0.051*AB - 0.038*AC + 0.0055*BC - 0.097*

[0075] A 2 - 0.066*B 2 - 0.086*C 2

[0076] Y 10-姜酚= 1.14 + 0.035 * A + 0.021 * B - 0.014 * C - 0.05 * AB - 0.023 * AC - 0.012 * BC - 0.12 *

[0077] A 2 -0.074 * B 2 -0.071 * C 2

[0078] Table 1-4 Regression analysis results of the zingerone model and regression coefficients

[0079]

[0080]

[0081] Note: P < 0.01 is extremely significant, indicated by **, P < 0.05 is significant, indicated by *, P > 0.05 is not significant, indicated by ns.

[0082] Regression analysis was performed on the zingerone model and regression coefficients. The results are shown in Table 1-4 and Figure 1 , and it can be seen that for this regression model, P < 0.001 (extremely significant), and the lack-of-fit term P = 0.080 > 0.05 (not significant), indicating that the model has a good fit and can be used to predict the corresponding regression values of the regression equation. At the same time, the regression coefficient R 2 = 0.9898, and the adjusted R 2 = 0.9768 (greater than 0.8000), indicating that 97.68% of the data can be explained by this model, showing that the equation has high reliability.

[0083] The magnitude of the F value is an important indicator for evaluating the influence degree of each variable on the response value. The larger the F value, the higher the contribution degree of the relevant model component to the response. When the significance test probability P < 0.05, it reveals that this variable has a significant influence on the response value and has statistical significance. By analyzing the relevant data, it can be seen that the linear terms of time and the ratio of sand to medicine have an extremely significant influence on zingerone (P < 0.01), and temperature has a significant influence on zingerone (P < 0.05). The main effect relationship of each factor is: C > A > B, that is, the ratio of sand to medicine > time > temperature. The quadratic interaction term AC has an extremely significant influence on zingerone (P < 0.01), BC has a significant influence on zingerone (P < 0.05), AB has no significant influence on zingerone (P > 0.05), and the influence degree of the quadratic interaction terms on zingerone is AC > BC > AB.

[0084] Table 1-5 Regression analysis results of the 6-gingerol model and regression coefficients

[0085]

[0086] Note: P < 0.01 is highly significant, denoted by **; P < 0.05 is significant, denoted by *; P > 0.05 is not significant, denoted by ns.

[0087] Regression analysis was performed on the 6-gingerol model and regression coefficients. The results are shown in Table 1-5 and Figure 2 , it can be seen from Table 1-5 that for this regression model, P < 0.001 (highly significant), and the lack-of-fit term P = 0.1154 > 0.05 (not significant), indicating that the model has a good fitting degree and can be used to predict the corresponding regression values of the regression equation. At the same time, the regression coefficient R 2 = 0.9879, and the adjusted R 2 = 0.9724 (greater than 0.8000), indicating that 97.24% of the data can be explained by this model, showing that the equation has a high reliability.

[0088] By analyzing the relevant data, it can be seen that the linear terms of time and sand-drug ratio have a highly significant effect on 6-gingerol (P < 0.01), and temperature has a significant effect on 6-gingerol (P < 0.05). Analyzing the main effect relationship of each factor shows that A > C > B, that is, time > sand-drug ratio > temperature. The quadratic interaction terms AB and AC have a highly significant effect on 6-gingerol (P < 0.01), BC has no significant effect on 6-gingerol (P > 0.05), and the degree of influence of the quadratic interaction terms on 6-gingerol is AC > AB > BC.

[0089] Table 1-6 Regression analysis results of 8-gingerol model and regression coefficients

[0090]

[0091] Note: P < 0.01 is highly significant, denoted by **; P < 0.05 is significant, denoted by *; P > 0.05 is not significant, denoted by ns.

[0092] Regression analysis was performed on this model and regression coefficients. The results are shown in Table 1-6 and Figure 3 , it can be seen from Table 1-6 that for this regression model, P < 0.0001 (highly significant), and the lack-of-fit term P = 0.0897 > 0.05 (not significant), indicating that the model has a good fitting degree and can be used to predict the corresponding regression values of the regression equation. At the same time, the regression coefficient R 2 = 0.9908, and the adjusted R 2 = 0.9791, indicating that 97.91% of the data can be explained by this model, showing that the equation has a high reliability.

[0093] By analyzing the relevant data, it can be seen that the linear terms of the ratio of sand to medicine, time, and temperature have extremely significant effects on the extraction rate (P < 0.01). The main effect relationship of each factor is: A > C > B, that is, time > ratio of sand to medicine > temperature. The quadratic interaction terms AB and AC have extremely significant effects on 8-gingerol (P < 0.01), and the effect of BC on 8-gingerol is not significant (P > 0.05).

[0094] Table 1-7 Regression analysis results of the 10-gingerol model and regression coefficients

[0095]

[0096]

[0097] Note: P < 0.01 is extremely significant, indicated by **; P < 0.05 is significant, indicated by *; P > 0.05 is not significant, indicated by ns.

[0098] Further regression analysis was performed on this model and regression coefficients, and the results are shown in Table 1-7 and Figure 4 , It can be seen from Table 1-7 that the P value of this regression model is < 0.0001 (extremely significant), and the lack-of-fit term P = 0.1689 > 0.05 (not significant), indicating that the model has a good fitting degree and can predict the corresponding regression values of the regression equation. At the same time, the regression coefficient R 2 = 0.9889, and the adjusted R 2 = 0.9746, indicating that 97.46% of the data can be explained by this model, indicating that the equation has high reliability.

[0099] By analyzing the relevant data, it can be seen that the linear terms of time and temperature have extremely significant effects on the extraction rate (P < 0.01), and the ratio of sand to medicine has a significant effect on 10-gingerol (P < 0.05). The main effect relationship of each factor is: A > B > C, that is, time > temperature > ratio of sand to medicine. The quadratic interaction term AB has an extremely significant effect on 10-gingerol (P < 0.01), AC has a significant effect on 10-gingerol (P < 0.05), and the effect of BC on 10-gingerol is not significant (P > 0.05).

[0100] Taking the maximum values of zingerone, 6-gingerol, 8-gingerol, and 10-gingerol as the optimization objectives according to the regression equation model, the predicted optimal conditions are: time 6.405 min, temperature 201.456 °C, and ratio of sand to medicine 6.616 times. According to the actual experimental conditions, the conditions were modified to time 6.4 min, temperature 201 °C, and ratio of sand to medicine 6.6 times. The results of 3 parallel experiments were basically consistent with the predicted results, confirming the good correlation between the predicted values and the experimental values. These experimental conditions were determined as the sand roasting process for small yellow ginger.

[0101] Preparation and Characterization of the Composition of Stir-Fried Rhizoma Zingiberis Recens and Citrus aurantium L. Flos in Sand

[0102] Preparation of extracts of Citrus aurantium L. Flos and stir-fried Rhizoma Zingiberis Recens: Accurately weigh 50 g of Citrus aurantium L. Flos and add it to an extraction flask, then add 10 times the amount of pure water. The mixture was refluxed at 100 °C for 60 minutes and concentrated to 350 mL to obtain the original Citrus aurantium L. Flos solution. Accurately weigh 50 g of stir-fried Rhizoma Zingiberis Recens, grind the stir-fried Rhizoma Zingiberis Recens into powder and sieve it, then transfer the obtained powder to a flask, add 10 times the amount of pure water, and the mixture was refluxed at 100 °C for 60 minutes and concentrated to obtain 300 mL of extract as the original stir-fried Rhizoma Zingiberis Recens solution. Finally, 9 combinations of the extracts of Citrus aurantium L. Flos and stir-fried Rhizoma Zingiberis Recens were prepared in different ratios (see Table 1-8).

[0103] The contents of the main components hesperidin, neohesperidin, synephrine, and naringin in the extract of Citrus aurantium L. Flos determined by HPLC were 0.020%, 0.119%, 0.139%, and 0.036% respectively. The contents of the main components zingerone, 6-gingerol, 8-gingerol, and 10-gingerol in the extract of stir-fried Rhizoma Zingiberis Recens determined by HPLC were 0.402%, 6.604%, 3.152%, and 3.041% respectively.

[0104] Table 1-8 Animal Experiment Combination Design of Citrus aurantium L. Flos - Stir-Fried Rhizoma Zingiberis Recens

[0105]

[0106]

[0107] Animal Experiment on the Weight Loss Effect of the Composition of Stir-Fried Rhizoma Zingiberis Recens and Citrus aurantium L. Flos in Example 3

[0108] Animal experiment: C57BL / 6J mice were randomly divided into 12 groups. The normal group received a standard diet, while the remaining groups received a high-fat diet. The treatment groups (A1 - C3) were given 9 ratios of the extract of Citrus aurantium L. Flos - stir-fried Rhizoma Zingiberis Recens. The positive control group was given orlistat (0.75 mg / mL), and the model control group was given an equal volume of normal saline. The experiment lasted for 6 weeks, and the whole experimental process was as Figure 5 shown. During the experiment, the mice were weighed weekly, and the weight change trajectory was recorded. All nine combinations of the extract of Citrus aurantium L. Flos - stir-fried Rhizoma Zingiberis Recens had a certain preventive effect on the obesity of the mice. It is worth noting that the weights of the A1 and C3 groups were significantly lower than those of the high-fat diet model group and were similar to those of the control group.

[0109] Biochemical index determination: After the administration ended, the mice were fasted for 12 hours. Then, blood samples were collected and centrifuged at 3500 rpm for 10 min at 4°C, and the supernatant was retained for subsequent detection. ELISA kits were used to detect the levels of total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c) in the serum. The ELISA results are shown in Figure 6 , the levels of total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol in the 1:1 compatibility group of extracts of Citrus aurantium L. and stir-fried Zingiber officinale Rosc. were lower than those in the single administration groups of extracts of Citrus aurantium L. or stir-fried Zingiber officinale Rosc., while the high-density lipoprotein level was higher than that in the single administration groups of extracts of Citrus aurantium L. or stir-fried Zingiber officinale Rosc. Figure 7 In, the results showed that all nine combinations of extracts of Citrus aurantium L. and stir-fried Zingiber officinale Rosc. had lipid-regulating effects on mice to varying degrees. Group A1 could significantly reduce the level of triglyceride (TG) (p<0.001), and the therapeutic effect of Group A1 was closest to that of the positive drug Orlistat. Group C3 could significantly reduce the levels of total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-c), and significantly increase the level of high-density lipoprotein cholesterol (HDL-c). We focused on Groups A1 and C3, and the two groups had the same formulation ratio (stir-fried Zingiber officinale Rosc. accounted for 63%).

[0110] Histopathological examination: Hematoxylin and eosin (H&E) staining is a standard technique for visualizing the histological structure of tissue samples (including liver tissue). Fresh tissue of the liver was fixed with 4% paraformaldehyde, sectioned after conventional dehydration and paraffin embedding. The sections were stained in hematoxylin solution for 5-10 min and soaked in running tap water for 5 minutes to remove excess stains. Then, the sections were stained in eosin solution for 1-2 min and briefly rinsed in running tap water to remove excess eosin. The sections were dehydrated through a series of graded ethanol: 50% ethanol for 1 min, 70% ethanol for 1 min, 95% ethanol for 1 min, and 100% ethanol for 2 min. Oil red O staining is a common technique for detecting neutral lipids in tissues. The epididymal fat was stained with oil red O staining solution after transparent sectioning. Lipid droplets will appear red, while the cell nuclei (if counterstained with hematoxylin) will appear blue. The pathological changes of each tissue were observed under an optical microscope (AXIOVert.A1, Zeiss). As Figure 5 shown in, the H&E staining results showed that in the mice of the high-fat diet (HFD) group, after 6 weeks of high-fat diet feeding, obvious fat accumulation occurred in the liver, and the fat droplets became round, squeezing the cell nuclei (shown by the green arrows). When CAVA-G combination was ingested simultaneously, the fat accumulation in the liver was significantly reduced. Oil red O staining showed that the epididymal fat cells of the mice in the high-fat model group were hypertrophied and obvious neutral fat accumulation occurred, while in Groups A1 and C3, the fat cells and fat accumulation were significantly reduced.

[0111] Serum metabolomics analysis: We performed non-targeted metabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS). 200 μL of ice-cold acetonitrile was added to 50 μL of serum, vortexed for 1 minute at 60 Hz, and then pre-cooled to 4 °C. Then the mixture was centrifuged at 13,000 rpm and 4 °C for 15 minutes. Subsequently, 200 μL of the supernatant was collected, pre-frozen at -80 °C, and lyophilized. The resulting dry sample was re-dissolved in 100 μL of solvent and centrifuged again at 13,000 rpm and 4 °C for 15 minutes. Finally, 80 μL of the re-dissolved sample was transferred to an injection vial for LC-MS analysis.

[0112] Metabolomics analysis was performed on an Ultimate 3000 UHPLC system (Dionex, Idstein, Germany) and a QExactive Orbitrap mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). The column temperature was set at 40 °C, and an ACQUITY UPLC HSS T3 column (2.1 mm × 100 mm, 1.8 μm, Waters, MA, USA) was used to separate the analytes. The mobile phase consisted of distilled water containing 0.1% formic acid (phase A) and acetonitrile (phase B). The flow rate was set at 0.3 mL / min, and the injection volume was 10 μL. The gradient elution program was as follows: 0 - 1 min, 2% B; 1 - 10 min, 2 - 100% B; 10 - 13 min, 100% B; 13 - 16 min, 2% B. The detailed mass spectrometry parameters are shown in Table 1-9.

[0113] Table 1-9 Mass spectrometry parameters for mouse serum metabolome analysis

[0114]

[0115] The LC-MS raw data was converted to the mzML format using the MSConvert GUI software (https: / / proteowizard.sourceforge.io) for subsequent analysis. Using the XCMS package (v3.22.0) in R (v4.3.2), we performed comprehensive data preprocessing, including peak detection, alignment, and gap filling, to generate a quantitative matrix. This matrix included basic information such as retention time (RT), mass-to-charge ratio (m / z), and feature intensity. To address signal drift and refine ion selection, the statTarget package (v1.30.0) in R was used.

[0116] Mass spectrometry ion filtering follows the following criteria: 1) exclude ions with more than 50% missing values in quality control (QC) samples; 2) filter out ions in non-QC samples if the missing values exceed 80%; 3) exclude QC samples with a relative standard deviation of more than 30%. Data normalization involves dividing each ion by the sum of the peak areas of all ions in the corresponding sample, multiplying by the average total ion area of the QC samples, and then applying a log2 transformation. For qualitative analysis, MS 2 The raw data were imported into MS-Dial (v4.9.22) software and matched with its built-in mass spectrum database for identification. The pre-processed data were then annotated.

[0117] The principal component analysis (PCA) scores of different groups are shown in the figure Figure 8 As shown in A. A clear separation trend between the high-fat diet (HFD) group and other groups can be clearly observed. At the same time, an OPLS-DA model has been established to find biomarkers between the high-fat model group and the A1 treatment group (such as Figure 8 (shown in B). A volcano plot in metabolomics is a graphical representation used to visualize the statistical significance and fold change differences of metabolites between two experimental groups. In a volcano plot, each point represents a metabolite, the x-axis usually represents the fold change, and the y-axis represents the statistical significance. The plot is volcano-shaped, with metabolites with large fold changes and high statistical significance at the extremes of the plot (i.e., the "top" of the volcano), while metabolites with insignificant changes are at the bottom. Figure 8 Figure C shows the volcano plot of metabolites between the A1 group and the high-fat model group. Compared with the high-fat model group, more endogenous metabolites in the A1 group were upregulated than downregulated. A heat map was constructed based on the metabolome data to visualize the differences between the A1 group and the high-fat model group. Hierarchical cluster analysis was performed to more clearly show the relationship between the groups (e.g. Figure 8 As shown in D). It can be seen that the samples in the group are automatically clustered into one category. At the same time, 12 differential biomarkers were identified between the A1 group and the high-fat model group, including bile acid, 3-dehydrocholic acid, cortisol, sebacic acid, malic acid, indoleacetic acid, deoxyguanosine monophosphate, adenylic acid, γ-glutamine, citric acid, indoxyl sulfate, and sucrose. The score map and volcano map of endogenous metabolites between the C3 group and the high-fat model group are shown in Figure 9 Middle A and Figure 9 Middle B. Compared with the high-fat model group, the up-regulated metabolites in the C3 group were more than the down-regulated metabolites. Figure 9As shown in C, 19 biomarkers between the C3 group and the high-fat model group were identified, including glyceric acid, cortisol, thymine, cholanic acid, 3-dehydrocholate, deoxyguanylic acid, adenylic acid, indoleacetic acid, betaine, 1-methylnicotinamide, sucrose, 4-hydroxycinnamic acid, uracil, 5-hydroxyindoleacetic acid, sphingosine, uric acid, octanoyl-L-carnitine, decanoyl carnitine, and hexanoyl carnitine.

[0118] After importing these 19 biomarkers into the KEGG database, the pathway enrichment results are clearly shown in Figure 10 Among them. Lipid metabolism, primary bile acid synthesis, and purine metabolism were identified as the pathways significantly intervened by the C3 group. The levels of 3-dehydrocholic acid, cholanic acid, indoleacetic acid, and indole sulfate were significantly decreased in high-fat mice. In contrast, the levels of these endogenous metabolites were significantly increased in the A1 group or the C3 group. Meanwhile, the levels of ceramide, hexanoyl carnitine, decanoyl carnitine, octanoyl carnitine, and sucrose were significantly increased in high-fat mice. The levels of these endogenous metabolites in the C3 group could be significantly decreased.

[0119] Gut microbiome analysis: Gut microbiome analysis based on 16S rRNA sequencing was performed on mouse feces. Fecal DNA was extracted using the TIANamp Stool DNA Kit. Then, the V3-V4 variable region of the 16S rRNA gene was amplified using the universal primers 341F (5'-CCTACGGGNGGCWGCAG-3') and 785R (5'-GACTACHVGGGTATCTAATCC-3'). After extraction, purification, and quantification, gut microbiota sequencing and library construction were performed on the Illumina NovaSeq PE250 platform (Illumina Inc., USA). High-quality reads were processed to remove barcodes and primers and to exclude chimeric sequences. The effective reads were clustered into operational taxonomic units (OTUs) with 97% identity. The OTUs were annotated against the Silva database using the RDP classifier tool. All diversity analyses and calculations and visualizations of the abundances at different OTU levels were performed using R software (v4.3.2). Alpha diversity indices were used to evaluate community diversity and richness, while beta diversity was evaluated using principal coordinate analysis (PCoA) based on the Bray-Curtis distance metric to reflect the separation between animal groups.

[0120] Data analysis: Experimental data were expressed as mean ± SD and analyzed using SPSS 26.0 (IBM, Armonk, NY, USA) and GraphPad Prism 8.0.1 (GraphPad Software, Inc., San Diego, CA, USA) software. Student's t-test or one-way analysis of variance (ANOVA) was used. Statistically significant differences were defined as p < 0.05. Serum metabolome data were analyzed and visualized using the R programming language and the Metaboanalyst website, and metabolites with a value greater than 1 were considered differential metabolites. Gut microbiota data were analyzed using ImageGP (https: / / www.bic.ac.cn / ImageGP / ), and biomarker species in different groups were identified by linear discriminant analysis (LDA) effect size (LEfSe) analysis (LDA > 2). The relationships between lipid indices (TC, TG, LDL-C, HDL-C), serum metabolome, and fecal microbiome were subjected to Spearman correlation analysis, and the results were presented in the form of a heatmap. According to the α-diversity analysis, there were no significant differences in the Chao1 and Ace indices among the high-fat model group, A1 group, and C3 group. However, there were significant differences in the Shannon and Simpson indices between the A1 group and the high-fat model group of mice (p < 0.05, Figure 11 in a and Figure 11 in b). This result indicates that a high-fat diet impairs the diversity of the gut microbiota, while A1 or C3 treatment improves the diversity of the gut microbiota. Figure 11 in c and Figure 11 The PCoA plots in d showed that the gut microbiota of the high-fat model group was significantly separated from that of the normal group, while the gut microbiota composition of the mice in the A1 group, C3 group, or positive drug group was similar to that of the normal group. As Figure 12 shown in a and Figure 12 b, at the phylum level, compared with the normal group, the relative abundance of Firmicutes in the high-fat model group increased significantly, the relative abundance of Bacteroidetes decreased significantly, and the F / B ratio was higher. After intragastric administration of A1 or C3, the F / B ratio decreased significantly (p < 0.01). It is generally believed that Bacteroidetes is negatively correlated with obesity, while Firmicutes is positively correlated with obesity. Therefore, a higher F / B ratio is considered an indicator for evaluating obesity. At the genus level, the relative abundances of the top 20 genera were calculated and a heatmap was generated. As Figure 12 shown in c and Figure 12 d, samples from the same group were clustered. The microbial genus abundances in the A1 group and C3 group were significantly different from those in the high-fat model group but similar to those in the normal group and positive drug group.

[0121] Figure 13It is the phylogenetic tree of the microbiota between group A1 or C3 and the model group (HFD). The bacterial community structure with significant differences between the HFD group and group A1 or C3 was further analyzed by the linear discriminant analysis (LDA) effect size method (LEfSe). The results showed that Akkermansia, Sutterella, and Chelativorans played important roles in distinguishing group A1 or C3 from the HFD group, while Oscillospira, Roseburia, Dorea, Staphylococcus, and Macrococcus were enriched in the HFD group.

Claims

1. A composition of Daidai flower and stir-fried Zingiber officinale Rosc. with sand, characterized in that, The composition is a liquid medicine prepared by water extraction using bitter orange flower and stir-fried small yellow ginger with sand as raw materials; In the said liquid medicine, calculated based on the raw materials, the mass ratio of stir-fried small yellow ginger to bitter orange flower is 30:70 to 87:13; In the said liquid medicine, calculated based on the raw materials, the concentration of stir-fried small yellow ginger is 0.03 - 0.12 g / mL, and the concentration of bitter orange flower is 0.0175 - 0.07 g / mL.

2. The composition of bitter orange flower and stir-fried small yellow ginger as described in claim 1, characterized in that, In the said liquid medicine, calculated based on the raw materials, the mass ratio of stir-fried small yellow ginger to bitter orange flower is 63:

37.

3. The composition of Citrus aurantium L. flower and stir-fried Zingiber officinale Rosc. with sand according to claim 1, wherein, The processing technology of stir-fried small yellow ginger is as follows: Weigh small yellow ginger and put it into river sand preheated to 180 - 220 °C for stir-frying with sand for 4 - 8 min, then take it out and let it cool, thus obtaining stir-fried small yellow ginger; Among them, the ratio of sand to medicine is 2 - 10.

4. The composition of Citrus aurantium L. and stir-fried Zingiber officinale Rosc. with sand as claimed in claim 3, wherein, The optimization method of the processing technology of stir-fried small yellow ginger is as follows: S1. Take clean small yellow ginger slices, 50 g for each portion. According to the influence of different stir-frying with sand conditions on gingerone, 6-gingerol, 8-gingerol, and 10-gingerol, based on the Box-Behnken experimental design principle, select three factors: time, temperature, and the ratio of sand to medicine. Each factor takes three levels marked as -1, 0, and 1, and use the response surface software Design Expert 10.0.3 for factor level design. The experimental scheme is as follows in the table: S2. Weigh the stir-fried small yellow ginger obtained under different stir-frying with sand conditions in S1 respectively, powder it, sieve the powder through a No. 3 sieve, accurately weigh it, put it into a conical flask respectively, add 30 mL of methanol, extract it by ultrasonic for 40 min, make up the loss with methanol, and filter it through a 0.45 μm microporous filter membrane to obtain the test solution; S3. Accurately weigh 1.23 mg, 2.76 mg, 2.02 mg, and 1.83 mg of 6-gingerol, 8-gingerol, 10-gingerol, and gingerone reference substances respectively, add 2 mL of methanol to each, ultrasonic process for 8 min, take 0.5 mL from each, and filter it through a 0.45 μm microporous filter membrane to obtain the reference substance stock solution; S4. Accurately absorb the 6-gingerol, 8-gingerol, 10-gingerol, and gingerone reference substance stock solutions to prepare a series of concentration mixed reference substance solutions, and inject and determine them according to the following chromatographic conditions: Chromatographic conditions: Using Thermo ODS C 18 chromatographic column; mobile phase A is acetonitrile, mobile phase B is 0.1% acetic acid aqueous solution, gradient elution: 0 - 10 min, 10 - 20% A; 10 - 60 min, 20 - 100% A; flow rate: 0.6 ml·min -1 ; detection wavelength: 280 nm; column temperature: 30 °C; injection volume: 10 μL; running time: 60 min; the number of theoretical plates is not less than 5000; Perform linear regression analysis with the concentration of each reference substance as the abscissa and the peak area as the ordinate to obtain the standard curves of 6-gingerol, 8-gingerol, 10-gingerol, and gingerone; S5. Use the chromatographic conditions in S4 to determine the test solution obtained in S2, substitute the peak area in the chromatogram into the standard curves obtained in S4, obtain the contents of gingerone, 6-gingerol, 8-gingerol, and 10-gingerol in the stir-fried small yellow ginger processed under different stir-frying with sand conditions, and through the response surface method, obtain that the optimal stir-frying time is 6.4 min, the stir-frying temperature is 201 °C, and the ratio of sand to medicine is 6.

6.

5. The preparation method of the composition of Daidai flower and stir-fried Rhizoma Zingiberis Recens with sand as claimed in claim 1, wherein, The said preparation method is as follows: Mix bitter orange flower with pure water, reflux and extract at 100 °C for 60 min, and concentrate to obtain the bitter orange flower extract; mix stir-fried small yellow ginger with pure water, reflux and extract at 100 °C for 60 min, and concentrate to obtain the stir-fried small yellow ginger extract; mix the bitter orange flower extract and the stir-fried small yellow ginger extract according to the ratio, and the obtained liquid medicine is the composition of bitter orange flower and stir-fried small yellow ginger.

6. The application of the composition of bitter orange flower and stir-fried small yellow ginger according to claim 1 in the preparation of drugs for preventing and treating obesity.

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